This paper proposes a multitask convolutional neural network (MTCNN) based on fine-grained images and multi-attribute fusion. The network mainly includes the following key links. First, the label input layer is added to the network; the input multiple labels are copied and separated, and then matched to multiple tasks with a fully connected layer. The Softmax loss function corresponding to the number of labels is added to backpropagate multiple tasks. Then, a fine-grained image in the original image is extracted by the combination of saliency detection and corner detection, and used as the input of MTCNN. The target features extracted by the neural network are more unique and distinguishable. Finally, the MTCNN uses the nonlinear activation function PReLu to further improve the classification accuracy of the network. This paper uses the MTCNN to perform multi-task parallel training in the Car Dataset and achieves a 10% improvement in the classification accuracy over the traditional single task. The results show that the MTCNN has high generalization performance and the accuracy of image classification is obviously improved.