To solve the problems of low recognition rate, high misrecognition rate, and long recognition time, the path recognition method of the regional education scale expansion based on the improved dragonfly algorithm is proposed. Through a variety of different behaviors utilized in the optimization process, the dragonfly algorithm model has been constructed. The step size and the position vector are introduced to update the dragonfly’s location. The dragonfly’s foraging behaviors are accurately simulated. Afterward, the dragonfly algorithm is combined with the flower authorization algorithm. The conversion probability is added, and the dragonfly’s global development ability is adjusted in real-time. Then, the dragonfly algorithm is improved. The improved dragonfly algorithm is employed to extract the features of the expansion path of the regional education scale. The improved support vector machine is utilized as a classifier to realize the recognition of the regional education scale expansion path. The experimental results denote that the proposed method has a high recognition rate of the regional education scale expansion path and can effectively reduce the misrecognition rate and shorten the recognition time.

中文翻译：

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基于改进蜻蜓算法的区域教育扩展路径识别

针对识别率低、误识别率高、识别时间长的问题，提出了基于改进蜻蜓算法的区域教育规模扩张路径识别方法。通过优化过程中利用的各种不同行为，已经构建了蜻蜓算法模型。引入步长和位置向量来更新蜻蜓的位置。蜻蜓的觅食行为被准确模拟。之后，蜻蜓算法与花卉授权算法相结合。加入转换概率，实时调整蜻蜓全球发展能力。然后对蜻蜓算法进行改进。采用改进的蜻蜓算法提取区域教育规模扩张路径特征。利用改进的支持向量机作为分类器，实现区域教育规模扩张路径的识别。实验结果表明，该方法对区域教育规模扩张路径具有较高的识别率，能够有效降低误识别率，缩短识别时间。