As the construction of talent teams in higher education institutions enters a new era, their digital transformation efforts have also faced urgent demands and valuable opportunities for development. This paper utilizes deep learning technologies such as image recognition, speech recognition, and text mining to construct a comprehensive digital visualization model framework tailored to the talent profiles of higher education institutions. The practical performance and efficiency of the proposed algorithm are validated using the CIFAR-10 and ImageNet datasets. The results show that compared to traditional algorithms, the deep learning image recognition algorithm proposed in this paper achieves higher recognition accuracy and shorter training time, not only improving computational efficiency but also reducing model storage requirements. Through visualization analysis, significant differences (P=0.001) were observed in the cultivation of six key competencies—professional ethics, cultural adaptation, teaching practice, scientific research, student guidance, and social service—among faculty members at University A before and after the implementation of the model.
