Urban Talent Demand Analysis Based on Deep Learning and Wavelet Threshold Denoising

摘要

Based on the relevant data of the talent market in A city, this paper analyzes the local talent demand situation. Firstly, all industries are divided into seven categories, and the local talent demand situation is analyzed from the three aspects of employment expectation, expected occupation and expected education background. The ratio of report ratio, industry employment demand and high academic qualifications are selected. The data is processed using wavelet threshold denoising and a NAR neural network model is established for prediction. In terms of employment, the relevant literature was consulted, and the employment rate, average salary and employment satisfaction data of Chinese students were collected, and the employment situation was predicted by combining the GM (1, 1) prediction model. Finally, starting from the number of applicants, the number of admissions, academic qualifications and the seven fast-growing industries in the city A, the SOM neural network model is established to compare the above-mentioned indicators of the city A with other cities in China, matching the same characteristics as the city A. Building an "intelligent forecasting and decision-making evaluation system" to help government departments and related institutions predict future urban development related information.