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.