A study on the psychological relief path of gender stress in women's workplace based on hierarchical analysis

Abstract

Abstract In this paper, we use variable prediction to assess mental health using a spurious nearest neighbor point algorithm to reconstruct mental trajectories, extract group feature vectors and map them into a high-dimensional spatial structure. The standard deviation of Gaussian function centroids is derived by combining the radial basis network input implicit layer node parameters. The hierarchical analysis method was used to split the psychological data, and the Lagrange multiplier method was used to reconstruct the psychological space, and it was found that the overall work stress index of women was 78.97 points, and there was no significant difference in the factors of the symptom self-rating scale. It is suggested that enterprises should offer psychological counseling courses, equip professional psychological counselors, scientifically guide employees’ psychological states, and create a gender-equal corporate culture atmosphere.