Under the background of rapid and comprehensive development of society and technology, the accommodation industry has shown a relatively prosperous development trend, which has promoted the reform and development of the hotel industry. The scale, quantity, and mode of hotel enterprises have undergone great changes, and there has been a structural surplus. Among them, the most obvious change is the integration of digital technology, which integrates technology and hotel services to form a new hotel format. Combined with a series of policy encouragement trends promulgated by the state, the hotel management and digital operation majors in higher vocational education are more targeted for the cultivation of talents, which can cultivate talents with innovative ability and industry skills in the hotel industry. In this context, it is very important to carry out an adaptive analysis on the professional psychology of students majoring in hotel management and digital operations, which can tap their potential and promote the improvement of teaching quality. Aiming at this subject, this work proposes a neural network (PPANet) for analyzing the psychological adaptability of students in this major. Aiming at the problem that CNN cannot capture the periodicity and trend of data, this work proposes a multiscale deep residual network. The network adds convolution kernels with different scales to learn data features and enhances generalization ability and improves accuracy through residual short-circuit structure and deep structure. Furthermore, based on ReLU, this work proposes an improved IReLU activation function with better learning properties. The systematic experiment verifies the feasibility of applying PPANet to the analysis of professional psychological adaptability of students majoring in hotel management and digital operations. © 2022 Juan Li.
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