对天然地震与人工爆破的波形记录，本文用ν-SVC支持向量分类机对由波形记录获取的香农熵特征进行了分类识别，效果较好；并对波形记录选取不同的信号窗长度，用ν-SVC支持向量分类机分别进行了识别检验。结果表明：窗长度对识别效果有影响，以窗长度为2000点的识别效果最好，识别率达98%。这也表明,在地震与爆破的识别中，合理地选取波形记录的信号窗长度也是重要的。

In this paper, the  ν-SVC algorithm is applied in the classification of earthquake and explosion basing upon Shannon entropy features extracted from seismic wave records. The recognition effect is approving. Several different window lengths of wave record are used for feature extraction. Classifications by  ν-SVC are carried out for recognition tests. The results show that window length is also an important factor for recognition rate. The best window length is 2000 sampling points which achieves 98% recognition rate. This means that appropriate window length of seismic signal may also be an important role in the classification of earthquake and explosion.