A Convolutional Neural Network-based Interaction Behavior Analysis and Creativity Evaluation System for Programming Games for 0-6 Years Old Children in Educational Big Data Environment

In this study, a multimodal analysis framework based on GCN is constructed to address the needs of interaction behavior analysis and creativity assessment of programming games for 0-6 years old children.A stack noise reduction self-coding neural network is used to recognize human gestures in images, and the feature representation of interaction behaviors is realized based on GCN, and the effectiveness of the method is proved by the experimental results on the two-player interaction behavior library.Construct a creativity evaluation system applicable to programming game scenarios for young children, and recruit 80 students aged 0-6 years old to carry out experiments.The students were classified using the GCN-based interaction behavior analysis model, and the weights of creativity evaluation indexes were determined by AHP.The fuzzy comprehensive evaluation method was used to evaluate and score the factors of creativity of the three categories of students, and the test results were verified with the help of the gray correlation method.The comprehensive evaluation scores of the three types of students are 2.006, 3.507 and 5.026, respectively, in which the creativity level of excellent learners is the highest and reaches the excellent grade.The normalized gray comprehensive correlation vector (0.3224, 0.3727, 0.3049) is close to the AHP weight vector (0.328, 0.357, 0.315) with a good assessment effect, and the research results provide a new technical path for behavior analysis and creativity development assessment in early childhood programming education.