Research on HR management based on RNN expectation theory

Abstract

RNN convolutional neural network is an important part of deep learning algorithm. Compared with fully connected neural network, the biggest difference is that the hidden units are not independent of each other. Not only the hidden layer neurons are irrelated to each other, but also the state of the current hidden layer cells is affected by the historical input data. This feature enables it to extract the temporal relationship of temporal data structure. One of the current outputs of one sequence is also related to the previous output. The specific expression is that the network will memorize the information before and apply it to the calculation of current output, the nodes between hidden layers are no longer connected but connected. Moreover, the input of hidden layer includes not only the output of input layer but also the output of hidden layer at the last time. RNN neural network is widely used in natural language processing (NLP): machine translation, speech recognition, image description generation, text similarity calculation. The DAFDC-RNN model in this paper is verified by the financial and real estate industry data from 2019 to 2020, which shows that the model has excellent stability and sensitivity. Compared with CNN model, it increases by 34.3% and 24.9%. The advantages of data processing as follow: it can process a large number of time series data, the processing capacity of data set is large, it can process 1000 + data, and it has more data base for time series prediction. The disadvantage as follow: Compared with CNN model, the working time of CNN model is increased by 48%, but the model has strong stability and adaptability. Taking the data from 2019 to 2020 as the input of DAFDC-RNN time series forecasting model, the salary forecast of real estate and financial industry in 2021 is obtained. Based on this data, the industry expectation of Finance and real estate industry under human resource management under expectation theory is reduced.