With the advent of technologies such as the Internet of Things, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart city environment. In this study, the current problems to be overcome by edge computing (EC) and the basic framework of edge computing are investigated to enhance the sustainable development of the smart city. Three 4aspects of the edge computing offload technology are explored including the software-defined network (SDN) controller, offload decision, and resource allocation, and a task offloading model of an edge computing network for a smart city is designed based on data preprocessing. Moreover, the energy-saving design and analysis of passive houses are carried out with Chengdu city as an example. The results reveal that the deployment scheme is feasible. In the passive house design scheme with natural ventilation, external shading, wall heat transfer coefficient of 0.63 W/(m2 K), and water storage roof, the annual energy consumption per unit area is the lowest, 18.97 kWh/m2, and its energy-saving rate is the highest, 0.77. The findings of the study provide some research experience for increasing the efficiency of smart city edge computing and boosting the smart city’s long-term development.

With the actual demand of international communication, intelligent translation of English has become a key direction of arti cial intelligence development in the current English eld. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classi ed into data categories, and the nal translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.

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