This study uses deep learning theory into the character recognition technology for Shui characters in ancient books, with the objectives of overcoming the instability of the high-pixel ancient Shui characters generative model and the need for large scale handwritten text data annotation among other issues. By constructing a multilayer adversarial neural network with a Laplacian structure, a clear generative model is established for original image data of Shui characters and a stable adversarial network model with multiple mapping relationships from coarse to fine is formed. Based on the analysis of the feature distance of Shui character image samples, the minimum inter-class spacing value and the optimal number of clusters are calculated. Combined with feedback from the classifier model, the optimal number of clusters in the clustering model is adjusted, an evaluation function with information entropy adjustment and clustering threshold convergence is constructed for the unsupervised labelling of Shui character image samples. In this paper, the feedback from the convolutional neural network is used to determine the algorithmic model of the hyperparameters for clustering annotation, and this structure is also designed to improve the recognition rate of handwritten Shui characters in ancient books. INDEX TERMS Shui characters, generative adversarial network, unsupervised labeling.

Intelligent vehicles have provided a variety of services; there is still a great challenge to execute some computing-intensive applications. Edge computing can provide plenty of computing resources for intelligent vehicles, because it offloads complex services from the base station (BS) to the edge computing nodes. Before the selection of the computing node for services, it is necessary to clarify the resource requirement of vehicles, the user mobility, and the situation of the mobile core network; they will affect the users’ quality of experience (QoE). To maximize the QoE, we use multiagent reinforcement learning to build an intelligent offloading system; we divide this goal into two suboptimization problems; they include global node scheduling and independent exploration of agents. We apply the improved Kuhn–Munkres (KM) algorithm to node scheduling and make full use of existing edge computing nodes; meanwhile, we guide intelligent vehicles to the potential areas of idle computing nodes; it can encourage their autonomous exploration. Finally, we make some performance evaluations to illustrate the effectiveness of our constructed system on the simulated dataset.