The traditional archive database retrieval method has long data processing time,low efficiency,and high cost and poor robustness when using traditional storage methods such as relational databases.Based on the distributed storage databass HBase,a fast retrieval model for engineering archive data based on classification recognition is proposed in the artide.This model mainly consists of modules for data classification and recognition technology and rapid data retrieval technology.The data classification and recognition technology addresses the shortcomings of traditional TF-IDF algorithms that are not sensitive to word position information.It uses inter class and intra class methods to improve the accuracy of classification and recognition,and combines them with Naive Bayes networks to improve the accuracy.The data fast retrieval technology module utilizes CNN and LSTM for data feature extraction,and uses a Hash algorithm to generate data Hash codes,improving retrieval speed.In experimental testing,the improved TF-IDF algorithm achieved the best accuracy,recall,and F1 values in different datasets.The retrieval time was reduced by more than 10%and the robustness was high.The experimental result indicate that the proposed method surpasses traditional methods and combines efficiency and stability.
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