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Feature point extraction is an important direction in the field of image processing. It has a wide range of applications in the fields of visual navigation, image matching, 3D reconstruction and so on. The feature point extraction method based on convolution neural network is the mainstream method at present. However, due to the constant size of the receptive field of the traditional convolution layer and the fixed geometric structure of the sampling area, the accuracy and robustness of feature point extraction are poor when the scale, viewing angle and illumination change greatly. In this paper, a self supervised feature point extraction network combining multi-scale and deformable convolution is proposed. Taking l2-net as the backbone of the network, this paper introduces multi-scale convolution kernel into the deep network to enhance the multi-scale feature extraction ability of the network and obtain the feature map of fine-grained scale information; Deformable convolution constrained by homography matrix is used to extract irregular feature regions, reduce the amount of computation, and solve the normalized constrained homography matrix to balance the impact of different sampling points on the results, cooperate with the convolution attention mechanism and coordinate attention mechanism added in the network to improve the feature extraction ability of the network. In this paper, comparative experiments and ablation experiments are carried out on hpatches data set. Compared with seven mainstream methods such as R2D2, the feature point extraction effect of this method is the best. Compared with suboptimal data, the feature point repeatability index (REP) is improved by about 1%, the matching score (M.S.) is improved by about 1.3%, and the average matching accuracy (MMA) is improved by about 0.4%. The method proposed in this paper makes full use of the deep information provided by deformable convolution and integrates the features of different scales to make the feature point extraction results more accurate and robust.