ABSTRACT According to the features of texts, a text classification model is proposed. Base on this model, an optimized objective function is designed by utilizing the occurrence frequency of each feature in each category. According to the relation matrix oftext resource and features, an improved genetic algorithm is adopted for solution with integral matrix crossover, transposition and recombination of entire population. At last the sample date of manufacturing text information from professional resources database system is taken as an example to illustrate the proposed model and solution for feature dimension reduction and text classification. The crossover and mutation probabilities of algorithm are compared vertically and horizontally to determine a group of better parameters. The experiment results show that the proposed method is fast and effective.
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