In complex environments， difficult samples and simple ones often coexist. The existing classification methods are mainly designed for difficult samples， and the constructed network causes a waste of computing resources when it is used to classify simple ones. However， network pruning and weight quantization couldn’t take into account both accuracy and storage cost. To promote the efficiency of computing resources with better accuracy， focusing on the spatial redundancy of input samples， this paper proposes an adaptive image classification network MAFDNet in complex environment， introduces the confidence as the classification accuracy of judgment， and puts forward the adaptive loss function composed of the content loss， fusion loss and classification loss at the same time. MAFDNet consists of three subnets. The input images are first sent to the low-resolution subnet， which effectively extracts low-resolution features. Samples with high confidence are first identified and removed from the network in advance， while samples with low confidence need to enter the subnet with higher resolution in turn. The high resolution subnet in the network has the ability to identify difficult samples. MAFDNet combines resolution adaptive and depth adaptive. Through experiments， the top-1 accuracy of MAFDNet is improved in CIFAR-10， CIFAR-100 and ImageNet data sets under the same computing resource conditions.