For the multi-mode radar working in the modern electronic battlefield, different working states of one single radar are prone to being classified as multiple emitters when adopting traditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classification method based on spatial data mining is presented to address the above challenge. Inspired by the idea of spatial data mining, the classification method applies nuclear field to depicting the distribution information of pulse samples in feature space, and digs out the hidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlation among all classes is established, which ensures classification accuracy of signal samples. Numerical experiments show that the presented method can effectively prevent different working states of multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy. For the multi-mode radar working in the modern electronic battlefield, different working states of one single radar are prone to being classified as multiple emitters when adopting traditional classification methods to process intercepted signals, which has a negative effect on signal classification. A classification method based on spatial data mining is presented to address the above challenge. Inspired by the idea of ​​spatial data mining, the classification method applies nuclear field to depicting the distribution information of pulse samples in feature space, and digs out the hidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlation among all classes is established, which ensures classification accuracy of signal samples. Numerical experiments show that the presented method can effectively prevent different working states of multi-mode emitter from being classified as several emitters, and achieve highe r classification accuracy.