A Chinese Text Classification System Based on Ada Boost-Bayes Algorithm

In view of the low accuracy of Chinese text classification algorithm, the classification algorithm is inefficient and the problem of low efficiency and low efficiency is proposed. Based on the adaptive algorithm, the proposed algorithm is proposed to improve the accuracy. The algorithm uses Bayes Naive and AdaBoost, and the advantages of the two algorithms are fused by the optimization of the structure. First, using the SMEL sequence of the word segmentation algorithm to segment the Chinese corpus and extract the feature words. Then, the enhanced Bias classifier is used to extract the feature of the text and generate the training classification matrix through the small sample training. Combined with the adaptive lifting algorithm, the simple classifier is weighted to ensure that the classification is stable and accurate. Experiments show that the error rate is lower than other algorithms, and the classification accuracy of the algorithm is more than 98%, and the F1 value is better than other classification algorithms.