Research on the Algorithm of Text Data Classification Based on Artificial Intelligence

Kang, Ying-jian; Ma, Lei

In view of the low recall of the traditional network text data classification algorithm, an artificial intelligence based network text data classification algorithm is designed. Before feature extraction, text information is preprocessed first, and word stem is extracted from English. Because there is no inherent space between Chinese words, word segmentation is carried out to complete the preprocessing of network text data. On this basis, an evaluation function is constructed to evaluate each feature item in the input space independently, and to reduce the dimension of the features of the network text data. Finally, the artificial intelligence method is used to classify the network text data, and the most similar training text is found through similarity measurement in the network text data training set. The experimental results show that the designed algorithm based on artificial intelligence has higher recall than the traditional algorithm, and can meet the needs of network text data classification.