Abstract
The development of the new liberal arts field places emphasis on the integration of disciplines such as humanities, engineering, medicine, and agriculture. It specifically highlights the incorporation of new technologies into the education and training of liberal arts majors like economics, law, literature, history, and philosophy. However, when dealing with complex talent data, shallow machine learning algorithms may not provide sufficiently accurate evaluations of the relationship between input and output. To address this challenge, this article introduces a comprehensive evaluation model for applied talents based on an improved Deep Belief Network (DBN). In this model, the GAAHS algorithm iteratively generates optimal values that are utilized as connection weights and biases for the restricted Boltzmann machines (RBM) in the pre-training stage of the DBN. This approach ensures that the weights and biases have favorable initial values. Moreover, the paper constructs a quality evaluation index system for creative talents, which consists of four components: knowledge level, innovation practice ability, adaptability to the environment, and psychological quality. The training results demonstrate that the optimized DBN exhibits improved convergence speed and precision, thereby achieving higher accuracy in the classification of applied talent evaluations.