Educational Information Retrieval Method for Innovative Entrepreneurship Training of Accounting Talents Based on Deep Learning

Abstract

In the education information retrieval of accounting talent innovation and entrepreneurship training, there may be issues such as inconsistent data quality, missing data, and outdated data, leading to a decrease in the performance of education information retrieval. To this end, a deep learning based accounting talent innovation and entrepreneurship training education information retrieval method is designed. Preprocesses such as text cleaning, Chinese word segmentation, vectorization of text, noise filtering, etc. are implemented for all texts. Aiming at the characteristics of high dimensionality and high sparsity of traditional multi classification text representation and classification methods based on bag of words model features, combined with the advantages of deep learning model to effectively extract high-level features, a deep belief convolution neural network model integrating deep belief network is proposed to extract low dimensionality, dense text high-level feature vector representation and implement text classification. Design a short learning model based on Actor Critic algorithm, use TCN to model the user to obtain the user’s intention, use two fully connected feedforward neural networks to represent the strategy and value function respectively, implement relevance matching for the query and document classification results, and realize the education information retrieval of accounting talent innovation and entrepreneurship training. The test results show that the design method has higher MAP, lower NDCG and ERR, and has good educational information retrieval performance.