In order to explore the relationship between cognitive function in children with learning difficulties and social environment, this study uses the Wechsler Intelligence Scale and the self-made general environment questionnaire to investigate 185 children with learning difficulties and compares them with 185 normal children, and gives attention test to 50 children with learning difficulties. The results show that family environment has a certain influence on the children with learning difficulties, they have a significantly lower verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ) and full scale intelligence quotient (FIQ), and the separation of VIQ and P IQ is common among them. As the children with learning difficulties grow older, their ability for abstract generalization tends to decline, which may be a characteristic of their intelligence development. This study aims to compare the functional differences in cortical regions between children with learning difficulties and children without from the perspective of cognitive neuropsychology, so as to provide effective assistance for children with learning difficulties.

Psychological health assessment and psychological problem identification essentially belong to problems of pattern recognition or nonlinear classification; its system contains complex nonlinear interactions among various factors, having basic characteristics of multivariable, multilevel, and strong coupling. An important problem in the field of artificial intelligence solved by convolutional neural networks (CNN) is to simplify complex problems, minimize the number of parameters, and thus greatly improve the algorithm’s performance. Therefore, CNN has outstanding advantages in establishing the assessment and analysis model of college students’ psychological health. This study determined the psychological health standards of college students, selected measurement tools for college students’ psychological state, elaborated the principles of psychological assessment based on text information, performed the sample set data establishment and data processing of the assessment and analysis model of psychological health, conducted network establishment, training, and simulation, carried out a case experiment and its result analysis, explored the cause analysis of college students’ psychological health problems, and finally discussed the prevention and intervention of college students’ psychological problems. The study results show that the input and output of the CNN-based assessment and analysis model of college students’ psychological health are their evaluation data and assessment results, respectively, and the optimal hyperparameters of the model are determined through fold cross-validation analysis to improve the model’s over-fitting problem. After the training is completed, the model can predict the changes in college students’ psychological state in the future through the psychological test data. The CNN uses supervised machine learning method to construct an assessment and analysis model of college students’ psychological health, and establishes the mapping relationship between college students’ personal background and their psychological health. The network error continuously adjusts network connection weight according to gradient descent algorithm to minimize its error, so that the convolutional layer and the pooling layer can learn the optimized feature expression of the input data.

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In order to realize the reliability evaluation of college students’ mental health quality, a model of college students’ mental health quality evaluation based on computational intelligence is proposed. The mutual information quantity is used as the benchmark parameter to measure the interaction between the two variables of college students’ mental health quality. The greedy algorithm to find the best feature subset for college students’ mental health quality assessment is designed. The feature sequence sampling and the reassembly model of college students’ mental health quality is constructed. The reliability assessment and nonparametric quantitative feature estimation are carried out, so as to complete the quantitative analysis and assessment of college students’ mental health quality. The test results show that this method has a high confidence level and good reliability. The evaluation results can accurately reflect the depression, stress, anxiety, and other emotions related to college students’ mental health, and have a good effect.