In view of the need of machine intelligence for tool recognition in man-machine communion environment, in order to improve the ability for home service robot to recognize affordances of tools, a modeling and classification method based on depth geometry feature spatial pyramid pooling is designed. In the off-line training phase, considering that the differences in the geometric shapes of various tools are more indicative for the tools themselves, firstly, the multi-types of geometric features are extracted from each tool depth map and fused to form the tool feature map. Then, the multi-scale feature patches are extracted and the spatial pooled pyramid is built based on cciPCA to construct the final tool feature vector. Finally, in the high-level semantic space, the tools classification model is trained based on the SVM classifier. In the online testing phase, the samples of spatial pooling are tested by a tool model trained offline. Experimental results show that the proposed method realizes recognition and classification of daily tools for home service robots, and the recognition accuracy of some tools reaches 97$%$ and above.