Research on the construction of a computational model for assessing students’ psychological state in ideological and political education of college students supported by multidimensional data

The accelerated pace of life and social competition become more and more intense, and the problem of psychological pressure faced by people in their study, work and life becomes more and more serious and common, this paper proposes a multi-channel physiological feature fusion method of psychological state assessment for the identification of students' psychological state in the ideological and political education of college students.The collected multidimensional physiological signal data, such as pulse and picoelectricity, are feature extracted, and the wavelet transform is used to reduce the noise of the physiological signals and realize the waveform filtering, and then the DS evidence theory is combined with the SVM, and the extracted physiological parameters of pulse and picoelectricity are used to realize the effective assessment of psychological stress.Experiments show that the method proposed in this paper of using wavelet decomposition coefficients instead of the original physiological signals as model input can improve the accuracy of psychological stress detection, and the MAPE value of psychological state assessment using the SVM-DS algorithm is 12.28%, which can realize the assessment of students' psychological state in ideological and political education of college students.