Abstract: ‍ ‍The application of deep learning technology has greatly improved the performance of SAR image target recognition， but its adaptability to changes in local components of vehicle targets in practical applications still needs to be strengthened. Using the inherent prior knowledge of the data， learning its inherent low dimensional subspace structure from high-dimensional semantic features can improve the generalization performance of the classification model under the condition of vehicle target variants. Based on the sparsity of target features， this paper proposes a sparse priori guided convolution neural network （CNN） learning SAR target recognition method （CNN-TDDL）. Firstly， CNN extracts high-dimensional semantic features of SAR image targets. Secondly， the sparse priori guidance module is used to learn the intrinsic low dimensional subspace structure of the target feature by using feature sparsity. The task driven dictionary learning （TDDL） layer of classification task represents the low dimensional subspace of the target feature in the form of sparse coding， and then uses the non negative elastic regular network to enhance the stability of sparse coding， so that sparse coding not only effectively represents the low dimensional subspace structure of the target， but also can extract more discriminative category features. Experiments based on moving and stationary target acquisition and recognition （MSTAR） datasets and synthetic and measured paired and labeled experiment （SAMPLE） datasets show that compared with traditional dictionary learning methods and typical deep learning methods， The identification accuracy of CNN-TDDL is improved by 0.85%~5.28% under MSTAR standard operating conditions （SOC）， and the model identification accuracy is improved by more than 3.97%， showing better generalization performance. The feature visualization analysis shows that sparse prior guidance module significantly improves the separability of feature representation of heterogeneous targets.