LAUSR

People usually use the method of job analysis to understand the requirements of each job in terms of personnel characteristics, at the same time use the method of psychological measurement to understand the psychological characteristics of each person, and then put the personnel in the appropriate position by matching them with each other. With the development of the information age, massive and complex data are produced. How to accurately extract the effective data needed by the industry from the big data is a very arduous task. In reality, personnel data are influenced by many factors, and the time series formed by it is more accidental and random and often has multilevel and multiscale characteristics. How to use a certain algorithm or data processing technology to effectively dig out the rules contained in the personnel information data and explore the personnel placement scheme has become an important issue. In this paper, a multilayer variable neural network model for complex big data feature learning is established to optimize the staffing scheme. At the same time, the learning model is extended from vector space to tensor space. The parameters of neural network are inversed by high-order backpropagation algorithm facing tensor space. Compared with the traditional multilayer neural network calculation model based on tensor space, the multimodal neural network calculation model can learn the characteristics of complex data quickly and accurately and has obvious advantages.