<b>An Intelligent Assessment of Children's Educational Mental Health Incorporating Convolutional Neural Networks and Self-Attention Mechanisms</b>

<b>1、The dataset used in this study, named "child_mental_health_dataset.csv", comprises data from approximately 3,000 elementary school students across 15 schools in Beijing, collected in 2022 as part of a children's educational mental health research project. It integrates multimodal data, including demographic information (age, gender, family economic status), 15 core mental health features (e.g., anxiety, self-confidence, responsibility, motivation, depression score) derived from the Children's Behavior Scale and Emotional Health Scale, teacher and parent observation ratings, and behavioral features extracted via optical flow (horizontal, vertical, and strain components) from video frames. The dataset supports regression tasks for predicting depression risk and classification tasks for emotion recognition, with simulated correlations aligning with observed psychological patterns (e.g., positive correlation between anxiety and depression, negative correlation with boldness). Stored in a CSV format with placeholder image paths for start and vertex frames, it is designed to facilitate intelligent mental health assessment using models like mini-ARCNN, offering a comprehensive resource for analyzing children's psychological states in educational settings.</b><br><b>2、The "Figures and Tables" folder contains all the visual and tabular data referenced in the study, including seven figures and two tables critical to the analysis of the proposed mini-ARCNN model for children's mental health assessment. The figures include the neural network architecture (Figure 1), methodology flowchart (Figure 2), self-attention mechanism structure (Figure 3), detailed mini-ARCNN architecture (Figure 4), model performance comparisons on MSE and R² (Figure 5), RMSE and MAE metrics (Figure 6), a heatmap of mental health feature correlations (Figure 7), and ablation experiment results (Figure 8). The tables present emotion recognition results for the proposed model (Table 1, 91.5% accuracy) and the MobliNet model (Table 2, 83.3% accuracy) across six expressions. These resources, stored as images and data files, provide a comprehensive visual representation of the study's methodology, experimental outcomes, and analytical insights.</b>