基于支持向量机的中文农业文本分类技术研究

高效地组织、分类信息，是提供个性化农业信息推荐服务的基础。根据农业文本信息特点，提出了一种基于线性支持向量机(Support vector machine，SVM)的中文农业文本分类模型，首先构建农业行业分类关键词库，通过特征词选择和权重计算，构建分类器模型，实现信息的自动分类。实验选取了1 071个测试文档，并按照种植业、林业、畜牧业、渔业进行分类。结果表明，分类准确率为96.5%，召回率为96.4%。实验结果高于贝叶斯、决策树、KNN、SMO等分类算法，将该模型应用于农业物联网行业信息综合服务平台，运行结果表明，该方法能够实现中文农业文本信息的自动分类，响应时间满足系统要求。

Classification Technique of Chinese Agricultural Text Information Based on SVM

In order to provide personalized services for agricultural information recommendation, it was needed to organize and classify information efficiently. According to the characteristics of agricultural texts, a Chinese agricultural text classification model was proposed based on linear support vector machine (SVM). Firstly, an agriculture-domain-based dictionary was built. Secondly, a feature vector was extracted and the weight for each feature in a vector was selected. Lastly, a text classification model was established. The model was tested on 1 071 documents which were belonged to four classes: planting, forestry, animal husbandry and fisheries. The results showed that the accuracy was 96.5% and the recall rate was 96.4%. Both of their performances were higher than those of the ones using other classification methods, such as the Bayesian, decision tree, KNN, SMO algorithm and neural network. The model was applied to the platform for agricultural internet of things (IOT) industry integrated information service. The performance showed that the method can automatically classify Chinese agricultural text information and the response time met the system requirements.