Arti-cial intelligence (AI) is a potentially transformative force that is likely to change the role of management and organizational practices. AI is revolutionizing corporate decision-making and changing management structures. (e visible e4ects of AI can be observed in key competencies and corporate processes such as knowledge management, as well as consumer outcomes including service quality perceptions and satisfaction. (is study aims to optimize the human resource management (HRM) process, reduce the workload of human resource managers, and improve work e7ciency. Based on AI digitization technology, a salary prediction model (SPM) is designed using a backpropagation neural network (BPNN), and the Nesterov and Adaptive Moment Estimation (Nadam) algorithms are integrated to optimize the model. Next, the content information of the resumes are used to predict the hiring salary of the candidates and validate the model. Results show that compared with other optimization algorithms, the -nal predicted result score of the Nadam optimization algorithm is 0.75%, and the training period is 186 s, providing the best optimization e4ect and the fastest convergence speed. Moreover, the BPNN-based SPM optimized by Nadam has good performance in the learning process and the accuracy rate can reach 79.4%, which veri-es the validity of the SPM. (e outcomes of this study can provide a reference for HRM systems based on data mining technology.
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