Research on Improving the Executive Ability of University Administrators Based on Deep Learning

Abstract

Over the years, experts have focused their research on ways to increase the executive capacity of university administrators. This is because only by improving the quality of execution of college and university administrative personnel can they actively execute various policies and measures, fully exploit their subjective initiative, and ensure the educational reform of colleges and universities. Increasing the executive capacity of administrative staff can help colleges and universities manage more effectively. Therefore, in the development process of higher education institutions, it is necessary to strengthen the execution of administrative staff, especially the need to adhere to the problem as the basic orientation. Take scientific and practical steps to strengthen administrative personnel’s executive ability in light of current issues with administrative management personnel’s executive power, and establish the groundwork for ensuring the quality of management work. Combining deep learning, this paper proposes a path to improve the executive power of college administrators based on deep learning. To begin, familiarize yourself with the deep noise reduction autoencoder model and support vector regression (SVR) theory and build the DDAE-SVR deep neural network (DNN) model. Then, input a small-scale feature index sample data set and a large-scale short-term traffic flow data set for experiments; then, assess the model’s parameters to achieve the optimal model. Finally, use performance indicators such as MSE and MAPE to compare with other shallow models to verify the effectiveness and advantages of the DDAE-SVR DNN model in the execution improvement path output of university administrators and large-scale data sets.