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In order to improve the accuracy of financial robot audit question answering, we propose, on the premise of processing corpus features, combining Bi-LSTM network and CRF to identify the domain entities, so as to solve the problem of low recognition rate of financial knowledge domain entities, and introducing the mechanism based on attention and CNN network to construct the multigranularity feature question-answering matching model. Finally, the above methods are verified by experiments. The results show that the AUC, MAP, and MRR increase by 0.74%, 0.85%, and 0.81%, respectively, indicating the feasibility of the improved method.

Implementation of Financial Audited Robot Question and Answer Technology of Feature Processing and Improved Bi-LSTM