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With the rapid development of video multimedia technology, video games have started to be applied in the field of education. A video game quality evaluation method needs to be designed to help teaching staff select video materials. In view of this, a Recurrent Neural Network (RNN)-based video game quality evaluation method for preschool education is proposed. Firstly, an improved attention mechanism null domain weight assignment method is proposed for the video quality evaluation problem. Then a video game quality evaluation model based on RNN is proposed. Finally, performance testing is conducted on the proposed quality evaluation method. The results show that in the comparison of fitness values, when the number of iterations is 4 or 16, the maximum fitness value and the average fitness value of the research method are 97 and 96 respectively. In the prediction of different video datasets, the research methods can achieve prediction accuracy of 0.958 and 0.947 for LIVE and CSIQ videos respectively. In the analysis of practical application effects, all students show significant improvement in language reading, writing, understanding, and spelling. The above results indicate that the research method has a faster convergence speed and prediction accuracy. It can effectively improve students' learning efficiency and provide new references for the education and teaching evaluation of preschool children. Introduction Entering the 21st century, with the popularity of applications such as short videos and live streaming, emerging multimedia applications are constantly refreshing people's past cognition. Images and videos have deeply influenced and changed our lives. People are constantly exposed to the services of image and video providers. Human life and work cannot do without digital images and online videos. In the field of preschool education, image materials and videos have played a more powerful role than text files, changing the previous educational model of preschool children [1], [2]. However, in the video processing such as collection, shooting, transmission, and encoding, it is often unavoidable to add distortion to video data. Preschool children cannot accept viewing low quality video materials and obtaining correct knowledge from them. However, manually judging video quality is a time-consuming and laborious task. Therefore, it is necessary to design objective video quality evaluation methods to complete this task. In the initial research on video quality evaluation algorithms, researchers chose full reference and semi reference quality evaluations. The manually extracted features are compared with the original undistorted video to predict video quality scores. “Non reference algorithms do not require initial images or video data to assist in judgment. More researchers are deeply studying non reference evaluation algorithms. At the same time, with the popularity of deep learning in the field of computer graphics, applying deep learning networks to non reference video quality evaluation is also an important topic [3], [4].” The video quality evaluation task aims to design a model that is simple enough and similar to human subjective evaluation to help humans judge the quality of video. The characteristics of the human visual system are inseparable from human subjective evaluation. Biological and psychological researchers have proposed attention mechanisms in studying the human visual system. It points out that when observe the outside world, people will quickly and automatically focus on the image regions of interest to the brain, helping the brain understand the surrounding environment [5]. In view of this, a quality evaluation method for preschool education video games based on RNN is proposed. Considering the strong feature extraction ability of deep learning, the RNN that integrates scene information to obtain the features of video frames is introduced. The impact of video scene switching on subjective evaluation during the process is analyzed. This method can avoid the impact of different factors on the experimental results to obtain a more reasonable evaluation model for the quality of preschool education video games. Section snippets Current research status of domestic and foreign scholars In recent years, the variety and quantity of video games for preschool education have continued to grow. The quality evaluation of educational enlightenment video games for preschool children has become a research focus for scholars in related fields. The continuous advancement of educational informationize has led to the widespread application of multimedia video game teaching courseware that integrates text, audio, video, etc. in preschool education. Among them, the application of neural Improved attention mechanism null field weight assignment method for video quality evaluation The experimental results of many studies indicate that neural networks have significant results in extracting video image features. The experimentally extracted image video features are more expressive when compared to the manually extracted video image features and eliminate many of the tedious problems associated with manual extraction. However, the game video dataset is large and contains many small sample datasets. For the learning model, the difference between the required sample size and Performance and application testing of an IRNN video game quality evaluation model The IRNN (Improved Recurrent Neural Network) neural network algorithm is used to construct a quality evaluation model for preschool education video games. Then the performance and application effects are analyzed. The fitness value, the loss function value, and the forecast dispersity of different data sets are mainly compared. To accurately verify the effectiveness of the model proposed in the study, the performance of literature [24], traditional RNN video teaching model, and the improved Conclusion To evaluate the quality of preschool education video games, an evaluation method based on cyclic neural networks is proposed. The experimental results show that both the RNN and the improved RNN no longer show any significant decreasing trend when the loss function reaches basic convergence after 600 iterations. The loss values of the proposed improved RNN algorithm range from 2 to 6, concentrated around the value of 3. Compared to traditional RNN, this is a smaller value. The improved RNN Funding The research is supported by: This work was supported by the Teaching Reform and Innovation Project of Higher Education in Shanxi Province in 2022; Multi-level integrated teaching design supported by multiple scaffolding -- The classroom teaching reform of preschool psychology with the orientation of learning for application (No. J20220469). Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References (24) L. Daniusevičiūtė-Brazaitė Assessing the format, duration and frequency of interactive game programs in terms of child engagement and motivation Baltic J. Sport Health Sci. (2022) I. Kyriakidis et al. Play and medical play in teaching pre-school children to cope with medical procedures involving J. Paediatr. Child Health (2021) W. Yunita et al. The Effectiveness of Project-Based Learning through Vlog to Improve Pre-Schoolers’ Vocabulary Mastery J. Obsesi: J. Pendidikan Anak Usia Dini (2022) Y. Wang et al. Predrnn: A recurrent neural network for spatiotemporal predictive learning IEEE Trans. Pattern Anal. Mach. Intell. (2022) S.P. Yadav et al. Survey on machine learning in speech emotion recognition and vision systems using a recurrent neural network (RNN) Arch. Comput. Meth. Eng. (2022) W. Zhang et al. Blind image quality assessment using a deep bilinear convolutional neural network. Blind image quality assessment using a deep bilinear convolutional neural network IEEE Trans. Circuits Syst. Video Technol. (2018) L.M. Po et al. A novel patch variance biased convolutional neural network for no-reference image quality assessment IEEE Trans. Circuits Syst. Video Technol. (2019) Q. Yan et al. Two-stream convolutional networks for blind image quality assessment IEEE Trans. Image Process. (2018) B. Chen et al. Learning generalized spatial–temporal deep feature representation for no-reference video quality assessment IEEE Trans. Circuits Syst. Video Technol. (2021) D. Ding et al. Advances in video compression system using deep neural network: a review and case studies Proc. IEEE (2021) W. Ren et al. Deblurring dynamic scenes via spatially varying recurrent neural networks IEEE Trans. Pattern Anal. Mach. Intell. (2021) D. Hyun et al. Deep learning for ultrasound image formation: CUBDL evaluation framework and open datasets IEEE Trans. Ultrasonics, Ferroelect. Frequency Control (2021) View more references Cited by (0) Recommended articles (6) Research article Multiple Composite Scenarios: A Game-Based Methodology for the Prevention of Mental Disorders Entertainment Computing, Volume 44, 2023, Article 100519 Show abstract Prevalence and incidence of mental disorders are on the rise. However, in comparison, the number of experts capable of treating them is not growing at the same rate. 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