With the wide application of wind energy as a clean energy source, to cope with the challenge of increasing maintenance difficulty brought about by the development of large-scale wind power equipment, it is crucial to monitor the operating status of wind turbines in real time and accurately identify the specific location of faults. In this study, a CNN-LSTM-based wind motor fault detection model is constructed for four types of typical faults, namely gearbox faults, electrical faults, yaw faults, and pitch faults of wind motors, combining CNN’s advantages of excelling in feature extraction and LSTM’s advantages of dealing with long-time sequence data, to achieve the simultaneous detection of multiple fault types. The accuracy of the CNN-LSTM-based wind turbine fault detection model reaches 90.06%, and optimal results are achieved for the effective discovery of yaw system faults, pitch system faults, and gearbox faults, obtaining 94.09%, 96.46%, and 97.39%, respectively. The CNN-LSTM wind turbine fault detection model proposed in this study improves the fault detection effect, avoids the further deterioration of faults, provides direction for preventive maintenance, reduces downtime loss due to restorative maintenance, and is essential for the sustainable use of wind turbines and maintenance of wind turbine service life, which helps to improve the operation and maintenance level of wind farms.

The cultural element is the minimum unit of a cultural system. The systematic categorizing, organizing, and retrieval of the traditional Chinese cultural elements are essential prerequisites for the realization of effective extracting and rational utilization, as well as the prerequisite for exploiting the contemporary value of the traditional Chinese culture. To build an objective, integrated, and reliable classification method and a system of traditional Chinese cultural elements, this study takes the text of Taiping Imperial Encyclopedia in Northern Song Dynasty as the primary data source. The unsupervised word segmentation methods are used to detect Out‐of‐Vocabulary (OOV), and then the segmentation results by the THULAC tool with and without custom dictionary are compared. The TF‐IDF algorithm is applied to extract the keywords of cultural elements and the Ochiia coefficient is introduced to create complex networks of traditional Chinese cultural elements. After analyzing the topological characteristics of the network, the community detection algorithm is used to identify the topics of cultural elements. Finally, a “Means‐Ends” two‐dimensional orthogonal classification system is established to categorize the topics. The results showed that the degree distribution in the complex network of Chinese traditional cultural elements is a scale‐free network with γ = 2.28. The network shows a structure of community and hierarchy features. The top 12 communities have taken up to 91.77% of the scale of the networks. Those 12 topics of the traditional Chinese cultural elements are circularly distributed in the orthogonal system of cultural elements’ categorization.

Green consumption is an important tool to accelerate the circular economy and promote sustainable development. The identification of critical influencing factors for green consumption is the key to promoting green consumption behavior (GCB). Firstly, based on the joint framework of theory of planned behavior (TPB) and the attitude–behavior–context (ABC) theory, we summarized 32 influencing factors from six dimensions: consumer attitude, cognitive factors, sense of responsibility, economic factors, government regulation, and green product supply. Secondly, the Delphi method was used to modify and optimize the initial influencing factor index. Thirdly, we constructed a social network analysis (SNA) model of influencing factors to determine the causal relationships between each influencing factor. All factors were divided into driving factors and result factors via the calculation of degree centrality, and the critical influencing factors and influencing paths of residents’ GCB were ultimately determined. Finally, based on the empirical research results, corresponding countermeasures and suggestions were put forward. The results show that the top five critical influencing factors include green purchase intention, willingness to pay, risk perception, green product certification, publicity and education, green product price, and green attribute information. Among them, green product certification, publicity and education, and green product price are critical driving factors in GCB.