Simulation of the Effectiveness Assessment Model for University Student Psychological Counseling Data Based on Deep Learning Algorithms

Psychological counseling for university students has received much attention in recent years, and how to effectively assess the effectiveness of a large amount of counseling data has become an urgent problem to be solved. This article aims to propose a validity assessment model for university student psychological counseling data based on deep learning (DL), and compare it with traditional decision tree (DT) algorithm to verify its superiority and practicality. Based on the characteristics of the data and the actual needs of assessment, this study will carefully design the network structure and optimize the model through training to ensure that it can accurately capture effective features from the data. Through comparative experiments, the DL algorithm proposed in this article is significantly superior to the DT algorithm in terms of prediction accuracy, operational efficiency, and model stability. The DL algorithm has shown significant advantages in evaluating the effectiveness of psychological counseling data for university students. This algorithm can more accurately assess the effectiveness of counseling data, provide strong support for psychological counseling services for university students, and promote progress and development in this field.