By analyzing the historical background of the times and summarizing the current situation of urban development research at home and abroad, we find that there is a lack of theoretical and practical research related to intelligent park cities. Therefore, this paper first starts with the connotation of park city theory, focuses on the improvement of planning methods, and studies the application paths of big data technology in the planning and construction of park cities. Taking Jinzhai Country as the research object, this paper further illustrates the applicability of this research method at the macrolevel. In the context of the park city, we analyze the problems of the current status of city construction in general and explore the path of “planning concept first, big data-assisted design” for innovative and intelligent city construction. According to the study, the overlook corridor control has an impact on the building height. In terms of landscape protection, overlook system simulation is the other key factor. In addition, the analytic hierarchy process is the basis for development intensity control. The results show that big data technology can assist in the landscape conservation, morphology formation, and efficient operation of Jinzhai Country Park City. Our aim is to achieve the protection and utilization of its ecological environment and natural resources and thus to comprehensively coordinate the multidimensional urban spaces and build a park city model. As an urban development model that meets the requirements of being “people-oriented, efficient, green, and aesthetic” in the new stage, park cities need to be faced by researchers in order to further realize the overall city goal and vision for the whole society to be smart. It also provides relevant design ideas and methods for the planning and construction of park cities in other similar cities or regions.

Urban resilience and urbanization have been researched wildly by urban researchers. The coupling relationship between the level of urban resilience and urbanization is a considerable reference to assess the quality of urban development. Based on the correlation of objective index data, it theoretically explains whether the urban resilience level is coupled with the urbanization level and the degree of coupling, providing advice and wisdom for the future high-quality urban development of Hefei. Objective. To explore whether there is coupling between the urbanization level and the urban resilience level and to explore what extent of the coupling is. Research Methods. The dimensionless method was mainly used to standardize the original statistical data, the entropy method can be chosen to obtain the weight of the indexes of urbanization level and urban resilience level, and the coupling coordination model was chosen to study the degree of coupling coordination. Research conclusions. From 2011 to 2013, the coupling coordinate on degree was low. The coupling coordination level of urbanization and urban resilience was moderately unbalanced in 2011, mild disorder in 2012, and primary coordination in 2013. However, in 2014 and 2015, the situation improved a lot, and the coordination degree was intermediate coordination. From 2016 to 2019, the coupling coordination degree was in the stage of advanced coordination.

For Chinese NER tasks, there is very little annotation data available. To increase the dataset, improve the accuracy of Chinese NER task, and improve the model's stability, the authors propose a method to add local adversarial training to the transfer learning model and integrate the attention mechanism. The model uses ALBERT for migration pre-training and adds perturbation factors to the output matrix of the embedding layer to constitute local adversarial training. BILSTM is used to encode the shared and private features of the task, and the attention mechanism is introduced to capture the characters that focus more on the entities. Finally, the best entity annotation is obtained by CRF. Experiments are conducted on People's Daily 2004 and Tsinghua University open-source text classification datasets. The experimental results show that compared with the SOTA model, the F1 values of the proposed method in this paper are improved by 7.32 and 7.98 in the two different datasets, respectively, proving that the accuracy of the method in this paper is improved in the Chinese domain.

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