Recently, convolutional neural network (CNN) have demonstrated strong performance and are widely used in many fields. Due to the large number of CNN parameters and high storage and computing power requirements, it is difficult to deploy on resource-constrained devices. Therefore, compression and acceleration of CNN models have become an urgent problem to be solved. With the research and development of automatic machine learning (AutoML), AutoML has profoundly impacted the development of neural networks. Inspired by this, this study proposes two automated accelerated CNN algorithms based on parameter estimation and genetic algorithms, which can calculate the optimal accelerated CNN model within a given accuracy loss range, effectively solving the error caused by artificially selected rank in tensor decomposition. It can effectively improve the compression and acceleration effects of the convolutional neural network. By rigorous testing on the MNIST and CIFAR-10 data sets, the accuracy rate on the MNIST dataset is slightly reduced by 0.35% compared to the original network, and the running time of the model is greatly reduced by 4.1 times, the accuracy rate on the CIFAR-10 dataset dropped slightly by 5.13%, and the running time of the model was greatly decreased by 0.8 times.