The results of the inverse synthetic aperture radar （ISAR） imaging in the framework of compressive sensing （CS） have the advantages of super resolution and no sidelobe interference. But the availability or appropriateness of the sparse representation of the target scene and the relatively low computational efficiency of image reconstruction algorithms limit the performance and application of the CS based ISAR imaging methods. Recently， the deep neural network （DNN） based under-sampled or incomplete signal reconstruction method achieve remarkable performance. DNN can extract the abstract feature representation from input data with the hidden layers and nonlinear activation layer. However， the existing DNNs are real domain models， and cannot be directly used in complex data processing. A complex value DNN （CV-DNN） with multistage decomposition ability is constructed by cascading different types of complex value network layers. Then， the CV-DNN is used for ISAR imaging. The CV-DNN architecture can extract and exploit the sparse feature of the target image extremely well by multi-layer nonlinear processing. The experimental results show that the proposed CV-DNN based ISAR imaging method can provide better shape reconstruction of target than state-of-the-art CS reconstruction algorithms and improve the imaging efficiency obviously.