Applications of Deep Learning in the Evaluation and Analysis of College Students’ Mental Health - Lanfeng Zhou (Discrete Dynamics in Nature and Society)
It is an important research direction of mental health discipline in the current era to evaluate and analyze college students’ mental health by using deep learning methods and form visual data characteristics and analyzable discipline conclusions. Based on this, this paper carries out the research method of convolutional neural network by using the research concept of deep learning. Firstly, the paper summarizes the fast intelligent analysis model based on the convolutional neural network system algorithm, classifies and summarizes the unique characteristics of college students’ mental health, and uses the convolutional neural network processing model to analyze, evaluate, and observe college students’ mental health combined with the big data theory. Secondly, through the expansion and utilization of multi-layer neuron self-coding neural network, the psychological health of college students is evaluated and analyzed in the psychological discipline, the discrete data structure is established by using the relevant psychological data, the psychological behavior of college students is analyzed, summarized, and classified, and the data model is filled to judge the mental health status of college students. Finally, through the design of confirmatory experiments, the results show that the college students’ mental health evaluation and analysis model based on deep learning is more efficient in individual data analysis. Compared with the mode of analyzing college students’ mental health through in-depth learning, the traditional psychological research method has a large workload and is not suitable for the universality and consistency of college students. This paper solves this problem and provides a reference for relevant research.