Psychology and Discipline Analysis of Special Education Based on Deep Learning

To address the challenges of low recall, poor precision in integration results, and suboptimal recommendation accuracy in the field of special education psychology and discipline analysis, a novel method based on deep learning is proposed. A data collection framework tailored for special education psychology and discipline analysis was developed, employing both manual and data center-based approaches to gather relevant data. The acquired data underwent preprocessing to generate candidate labels, with TextRank scores, TF-IDF weights, and positional information weights calculated independently. A comprehensive scoring mechanism was applied by integrating multiple features, arranging the results in descending order to produce document labels. Utilizing a graph convolutional network from deep learning, a recommendation model for special education psychology and discipline data was constructed based on the generated document labels. This facilitated the effective analysis of special education psychology and discipline resources. Experimental outcomes demonstrate that the proposed method achieves maximum recall and precision rates of 98.8% and 98.7%, respectively, with a consistent recommendation accuracy exceeding 93%. The method exhibits excellent practical application performance.