This study researches on the application of SVM in real estate investment project risk forecasting and analysis. Support vector machine （SVM） is a novel machine learning technique based on the statistic learning theory （SLT） and structure risk minimization （SRM） principle. Through selecting castigatory parameter C and kernel function, the evaluated risk can be proved that it is consistent with the data tendency through simulation. Through the positive research the SVM algorithm can be shown very good forecasting ability in the risk predicting field%本项目侧重于研究支持向量机在房地产投资项目中的风险预测与分析的应用。支持向量机是基于统计学习理论和结构风险最小原则基础上的新型机器学习技术。通过选取惩罚系数C和核函数，经过模型初步评价的风险能够和最终数据模拟结果相一致，通过对支持向量机的实证研究表明此种算法在风险预测领域具有较好的风险预测能力
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