A Hybrid Deep Learning and Optimization Model for Enterprise Archive Semantic Retrieval

By searching for and summarizing the relevant information of the enterprise, we can build relevant knowledge maps, supplement and enrich the existing knowledge base, and support existing experiments and subsequent algorithm improvements. The extracted input text of enterprise archives is described via relation extraction and semantic analysis to improve the efficiency of archive retrieval and reduce the cost of communication. On the basis of the analysis of previous research, an enterprise archive semantic retrieval algorithm based on deep learning technology is constructed, that is, the BERT + BiGRU + CRF + HHO_improved model, to extract the relevant information of the enterprise. In the model, the Bidirectional Encoder Representations from Transformers (BERT) model is used to preprocess the Chinese word embedding, and the question-and-answer data are generated from the actual enterprise file database. Next, a Bidirectional Gated Recursive Unit (BiGRU) is used with the attention mechanism to capture the contextual features of the sequence. The Conditional Random Field (CRF) classifier is subsequently used to classify the text related to the enterprise archives, and the obtained data are labeled in sequence. Moreover, the swarm intelligence algorithm is introduced to dynamically optimize the model parameters and data processing strategies further to improve the generalization ability and adaptability of the model. The Harris Hawks Optimizer Improved (HHO_improved) algorithm is used to optimize the parameters of the CRF module to increase the performance and efficiency of named entity recognition. On the independently constructed dataset, the advantages of our algorithm are verified via comparative experiments with a variety of semantic matching algorithms and ablation experiments on the CRF and HHO_improved. The CRF and HHO_improved play essential roles in improving model performance. The obtained knowledge extraction results are expected to supplement and enhance the existing knowledge base, simplify the workflow, assist the enterprise’s dynamic production task management, and improve the efficiency of enterprise operations. The proposed algorithm achieves an accuracy improvement of 36.33%, 43.88%, 15.24%, and 12.41% over the BERT, BiGRU, BERT + BiGRU, and BERT + BiGRU + CRF models, respectively.