Abstract: With the continuous development of computer technology,many computer technologies have used intelligent algorithms to improve their intelligence level.Among them,lightweight deep learning network algorithms are one of the most frequently used,and many fields have used this algorithm to improve their production efficiency.However,current lightweight deep learning network algorithms still have drawbacks such as large algorithm scale and poor feature extraction performance.In order to solve the above problems,this study focuses on the One-dimensional convolutional neural network algorithm in deep network learning algorithms,and uses pruning algorithms to design lightweight convolutional neural network algorithms in order to optimize their performance.The study first compared the lightweight convolutional neural network algorithm with traditional algorithms,and the results showed that the speed of the lightweight algorithm was improved by nearly three times,reaching 3.7bps.At the same time,the storage requirements and energy consumption of the algorithm were significantly reduced,with energy consumption only 12.3%.Comparing the lightweight convolutional neural network learning algorithm of pruning algorithm with other lightweight algorithms,the results show that the average detection accuracy of this algorithm for different data is over 95%,far higher than other algorithms.The feature extraction effect of this algorithm is also significantly better than other algorithms,and the running time of this algorithm is only 4.98ms,far lower than other algorithms.From the above results,it can be concluded that the proposed pruning algorithm lightweight design method can improve the performance of deep learning network algorithms.

Key words: Deep learning network algorithm, Pruning algorithm, Lightweight, CNN