The development of digital competence has become an important part of higher education, and digital competence assessments have attracted considerable attention and concerns. Previous studies in this area mainly focused on self-reporting and manual review methods such as questionnaires, which offer limited assessment value. To solve this issue, this study uses natural language processing (NLP)--a current promising artificial intelligence (AI) technology--to analyze syllabi for assessing digital competence in universities. Analysis results show that the proposed method can achieve an average accuracy and consistency of over 80% with excellent efficiency. Moreover, the method demonstrates high consistency with manual evaluation results (kappa > 0.6) and enables automated large-scale objective assessment. In brief, the results suggest that the proposed method is efficient, effective, and reliable, making it a valuable solution for digital competence assessment. We accordingly explore the application expansion of this method in building the digital competence of universities. Furthermore, we discuss the theoretical, methodological, and applied contributions of this study.