At present, the employment pressure of college students is severe, so there is an urgent need to evaluate the capabilities that enterprises value. This is conducive for enhancing the competitiveness of students and helps employers select qualified personnel. Firstly, we combine the analytic hierarchy process (AHP) and fuzzy evaluation to evaluate college students' comprehensive quality. In order to solve the problem that the fuzzy system cannot automatically adjust the parameters of membership functions, effectively combining the advantages of the fuzzy theory and neural network architecture, we propose an improved comprehensive evaluation algorithm. We also design a wavelet neural network with time-frequency localization to better simulate nonlinear functions and predict suitable positions for students. Experimental results show that the competency evaluation model based on the improved fuzzy neural network algorithm and the wavelet network job matching model are able to improve system accuracy and self-adaptive ability, and the evaluation is objective, which have a guiding significance for the competence evaluation and employment options of the students.