Next-generation networks are data-driven by design but face uncertainty due to various changing user group patterns and the hybrid nature of infrastructures running these systems. Meanwhile, the amount of data gathered in the computer system is increasing. How to classify and process the massive data to reduce the amount of data transmission in the network is a very worthy problem. Recent research uses deep learning to propose solutions for these and related issues. However, deep learning faces problems like overfitting that may undermine the effectiveness of its applications in solving different network problems. This paper considers the overfitting problem of convolutional neural network (CNN) models in practical applications. An algorithm for maximum pooling dropout and weight attenuation is proposed to avoid overfitting. First, design the maximum value pooling dropout in the pooling layer of the model to sparse the neurons and then introduce the regularization based on weight attenuation to reduce the complexity of the model when the gradient of the loss function is calculated by backpropagation. Theoretical analysis and experiments show that the proposed method can effectively avoid overfitting and can reduce the error rate of data set classification by more than 10% on average than other methods. The proposed method can improve the quality of different deep learning-based solutions designed for data management and processing in next-generation networks.

With the rapid development of information technology, industry and service industries have achieved rapid development in recent years. Then, looking at the development of agriculture, the popularity of informatization lags far behind industry and service industries, directly hindering the digital development of agriculture. Starting from the current agricultural machinery driving operation scene, this paper carried out a simplified research on the traditional agricultural machinery driving operation method through the agricultural machinery kinematics model, and based on the related theory of deep reinforcement learning to study the agricultural machinery path tracking in the agricultural operation scene, it carried out the controller design, built the agricultural machinery autonomous path tracking framework operating mechanism under deep reinforcement learning, and further researched through experimental design and found that the agricultural machinery autonomous path tracking control can achieve better automatic control after empirical learning. I-DQN algorithm enables agricultural robots to adapt to the environment faster when performing path tracking, which improves the performance of path tracking. It has important guiding significance for further promoting the automatic navigation and control of agricultural machinery to realize the efficient operation of agricultural mechanization.

Action recognition in Taekwondo competitions and training is an important task, which can provide a very valuable reference factor for technicians, athletes, and coaches. We propose a graph convolution framework with part of the perception structure to recognize, decompose, and analyze Taekwondo actions. Taking advantage of the long short-term memory of a part of the perception structure, the recognized Taekwondo actions are marked in time series, and then features are extracted from the graph convolution level to obtain the spatial and temporal associations between joints. Predict the action category and perform score matching based on the manual tag database. Finally, it is verified on our self-made Taekwondo competition data set. Our method has an average accuracy of 90% in action recognition, and an average action score matching rate of 74.6%. The accuracy of action recognition is high, which provides great assistance to Taekwondo e training and competitions.

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