Anxiety is usual y generated because of the threatened feeling. The data of electrocardio, respiration, blood volume pulse and skin conductance signals were col ected. The arithmetic of Relief were used for the feature selection and combined with k-Nearest Neighbor (kNN) arithmetic and Support Vector Machine (SVM) arithmetic for classification. The results show that the combination of Relief-SVM is better than combination of Relief-kNN on the recognition of anxiety state. The emotion recognition based on multi-physiological signals is superior to that based on one single signal.%焦虑是一种在感到被威胁的环境中产生的复杂的心理过程。该文通过任务驱动的焦虑情绪诱发实验，采集被试心电、呼吸、血容量搏动、皮肤电四种生理信号数据，结合Relief算法对特征进行选择，并结合k近邻算法（kNN）和支持向量机（SVM）算法，对平静状态和焦虑情绪状态进行识别分类。结果表明，对于焦虑情绪状态下的情绪识别，Relief-SVM算法优于Relief-kNN算法；利用多生理参数进行情绪识别优于单一生理参数。
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