Bio-inspired Deep Neural Network (DNN) is a revolutionary breakthrough in artificial intelligent field. However, the lack of storage space as well as computing capacity caused by the explosive increase of the model weights not only keeps DNN apart from its original inspiration, but also makes it difficult to deploy DNN on embedded/mobile devices. In order to solve this problem, the biological selection principle in the evolution was studied, and a novel neural network algorithm based on "evolution" + "randomness" + "selection" was proposed. In this method, the size of the existing models were greatly simplified on the premise of maintaining the basic framework of the existing neural network models. First, the weight parameters were clustered. Then, based on the cluster centroid values of the parameters, the random perturbation was added to reconstruct the parameters. Finally, the image classification and object detection were performed on the reconstructed model to realize the accuracy test and model stability analysis. Experimental results on ImageNet dataset and COCO dataset show that the proposed model reconstruction method can compress the sizes of four models, including Darknet19, ResNet18, ResNet50 and YOLOv3, to 1/4-1/3 of the original ones, and under the condition of 1%-3% performance improvement in the test accuracy of image classification and object detection, there is the possibility of further simplification.