Research on the model of volume feature extraction based on text feature extraction and random forest algorithm

In this paper, a volume feature extraction model combining stochastic forest and Bayesian theory, and using TF-IDF (Term frequency-inverse Document Frequency) method for text feature extraction is proposed. Firstly, the accuracy of subsequent feature extraction is ensured by the operation of denoising, word segmentation and stopping word removal of the text content. Then, TF-IDF technology is used to extract the most representative feature words from the pre-processed text, and form an efficient feature vector representation. On this basis, combining the advantages of random forest and Bayesian theory, a hybrid classifier is constructed. Random forests are used to capture nonlinear relationships in data, while Bayesian theory provides a probabilistic framework that enables models to make more reasonable predictions under conditions of uncertainty. The experimental results show that the model has higher performance in the key indexes such as accuracy, recall rate and F1 value.