Research on a Label Association Algorithm Based on Feature Engineering and BILSTM Technology

To provide the industry with an algorithm capable of accurately calculating comment statement tags, this paper proposes a research method based on feature engineering and BILSTM technology. First, the paper uses big data crawler technology to collect evaluation statements from relevant websites. Next, it utilizes the jieba segmentation technology with Directed Acyclic Graph (DAG) to segment the evaluation statements. Then, feature engineering techniques are employed to merge and reduce feature attributes, creating a regularized and standardized input data matrix. Finally, the BILSTM mathematical model is used to train model parameters and predict data based on training data. The prediction results show that the accuracy of this method is over 80% and it can automatically calculate tags.