支持向量机(SVM)普遍应用在机器学习领域的学习算法,广泛用于分类学习。支持向量机也应用在很多实际应用领域中。该算法也广泛地应用在煤炭系统的分类预测工作中。随着数字时代的发展,煤炭系统的数据规模也呈现大规模增长趋势。针对海量规模数据,传统的支持向量机模型不能有效地完成煤炭系统中数据的分类、回归等工作。文章针对大规模数据处理困难的问题,提出了分布式支持向量机模型。该模型针对现有流行的云计算平台,在该平台下构建基于Hadoop分布式计算框架的分布式模型,该分布式支持向量机模型能够高效、快速地完成真实数据的分类或回归任务,具有很高的效率。文中的实验部分通过大量的实验数据进一步证明了文章提出算法的可行性。 Support Vector Machine (SVM) is widely used in machine learning domain learning algorithm, widely used in classification learning. Support vector machines are also used in many practical applications. The algorithm is also widely used in the classification of coal system prediction work. With the development of the digital age, the data size of the coal system also shows a large-scale growth trend. For mass data, the traditional support vector machine model can not effectively complete the data classification, regression and other work in the coal system. In order to solve the problem of large-scale data processing, a distributed support vector machine model is proposed. According to the existing popular cloud computing platform, this model constructs a distributed model based on Hadoop distributed computing framework. This distributed support vector machine model can accomplish the real data classification or regression task efficiently and quickly. High efficiency. The experimental part of the paper further proves the feasibility of the proposed algorithm through a large number of experimental data.