中文摘要：

【目的】对适用于特征选择的算法进行研究，有效提高文本分类精度和效率。【方法】结合特征选择特点，以可拓理论为基础构造小生境量子粒子群算法，通过改进增强算法搜索能力，将不同的特征选择方法用于文本分类并进行比较。【结果】实验结果表明，与IG、MI等方法相比，基于可拓小生境量子粒子群算法的特征选择在文本分类中取得了较好效果，算法的求解精度得到明显提升。【局限】所提出的特征选择方法在时间效率上有待改善。【结论】对量子粒子群算法的改进措施有效提高了算法的搜索能力，在特征选择的应用中达到较好的效果。

英文摘要：

[Objective] This study proposes an algorithm for feature selection aiming to improve the precision and efficiency of text classification. [Methods] First, we selected features based on their characteristics. Then, we constructed the algorithm with extension theory to strengthen its searching ability. Finally, we compared the performance of different methods for text classification. [Results] Compared with IG, MI and QPSO, the proposed algorithm had better accuracy in feature selection. [Limitations] The efficiency of our algorithm needs to be improved. [Conclusions] The modified QPSO Algorithm is an effective way to select features.