Research on Optimization Model of Cognitive Enhancement Algorithm in Intelligent Data Analysis System

This paper discusses the optimization model of cognitive enhancement algorithm in intelligent data analysis system, aiming to improve the accuracy of early diagnosis and individualized treatment of mental disorders by integrating multi-modal physiological signals. The model integrates an efficient physiological signal acquisition system to capture multi-modal data such as electroencephalogram (EEG), skin electrical activity (GSR) and heart rate variability (HRV) in real time to achieve a comprehensive reflection of individual cognitive status and emotional fluctuations. At the algorithm level, random forest is introduced as the core cognitive diagnostic tool, supplemented by an innovative Q matrix structure to guide feature selection and decision tree growth. It not only integrates traditional psychometric parameters, but also introduces multi-modal physiological signal feature weights, significantly enhancing the model's ability to capture complex associations between mental states and physiological indicators. Experimental verification shows that the optimized model has excellent performance in mental health assessment, the diagnostic accuracy is improved by about 20% compared with the traditional method, while the processing speed is greatly improved. The response time is reduced by nearly 30%, showing good generalization ability, and the prediction performance of unknown test data is maintained at a high level.