Construction of ASD Intervention System Based On Neural Network Model

Aiming at the problem of early identification and personalized intervention of the behavioral patterns of autism spectrum disorder(ASD), this paper introduces a behavior prediction and intervention system based on neural networks, aiming to improve the accuracy of behavior prediction for children with autism and achieve personalized intervention. First, this paper constructs a behavior pattern prediction and intervention system based on the CNN-LSTM model, and conducts in-depth analysis and extraction of the behavioral characteristics of children with autism through multimodal data collection (including video surveillance, sensor data, etc.). Then, based on CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory), a comprehensive behavior pattern prediction model is constructed, which can identify the key behavior patterns of children with autism from different types of behavior data. Finally, combined with the output of the prediction model, a personalized intervention system is designed, which can dynamically adjust the intervention strategy in real time and monitor the intervention effect. The performance of the CNN-LSTM model in predicting behavioral patterns of autistic children far exceeds traditional machine learning methods (such as SVM (Support Vector Machine) and decision trees). Specifically, the behavioral prediction accuracy of the CNN-LSTM model is 92.5%, which is significantly higher than SVM (85.3%) and decision tree (79.1%). In summary, the behavioral pattern prediction and intervention system based on neural networks has broad application prospects and potential in early intervention for autistic children.