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In recent years, China has been paying more and more attention to the development of the sports industry, and many sports are no longer seen as a mere sport, but can be developed into an industry and play an important role in the development of the economy. This paper examines the application of multi-source big data mining techniques in the analysis of basketball economic management. Firstly, through multi-source big data mining technology, we collect various factors that influence the development of basketball economic industrialization, use Hash Tree-based Apriori algorithm to mine various influencing factors for basketball economic industrialization, and analyze the correlation between each influencing factor. The association rule mining results are then used to analyze the relationship between the key influencing factors and the industrialization of the basketball economy. This paper examines various aspects of the Chinese basketball league market, including the management system, market operations, and talent flow, and compares them with the foreign basketball industry models, in order to analyze the operation of China's basketball industrialization and develop corresponding countermeasures to improve basketball economic management based on the results of the study. [ABSTRACT FROM AUTHOR]

With the booming of big data in finance, data mining technologies, as a new method of data statistics, have made superior economic applications available to researchers. Based on the relationship between shadow banking and interest rate liberalization, this paper intended to analyze the bidirectional relationship between shadow banking and interest rate liberalization using big data mining technologies. The relationship between data mining and the economic application of shadow banking was proven using data from 2014 to 2021. On this basis, this paper identified the bidirectional influence between shadow banking and interest rate liberalization. The findings show that shadow banking has positive contributions to the liberalization of interest rate, and the interest rate distortion resulting from interest rate control also drives the development of shadow banking. Moreover, feasible suggestions have been proposed for supervision on policies. [ABSTRACT FROM AUTHOR]

With the continuous acceleration of the urbanization process, rural areas have gradually put forward construction plans for new rural forms. Pastoral complex is a construction mode of characteristic townships and rural complexes, which has become a new form of social development today. Pastoral complexes provide a new model of new rural construction, and its development is still in the preliminary stage for China. The construction of pastoral complexes involves rural landscape planning, economic management, etc. It is difficult for rural managers to process these complex data characteristics. Big data technology is affecting people's lives, and the discipline of landscape architecture is also deeply affected by big data. This paper mainly uses data mining technology and neural network method to carry out feature mining and prediction on the landscape planning and management methods of beautiful rural pastoral complexes. In this paper, a data mining algorithm is used to classify the landscape types and locations of rural complex, and the neural network method is used to predict the landscape characteristics. The results show that the methods of data mining and neural network are feasible and reliable both in the proportion of classification and in the prediction of landscape planning. The maximum error of the prediction of landscape design is 2.87%, and the minimum error is only 0.79% using the neural network method, which is an acceptable error range. [ABSTRACT FROM AUTHOR]

Industrial and economic development in China are both advancing at a rapid rate. China is currently the world's second-largest economy. The immediate result of China's continuous economic development is a steady improvement in people's living standards, the great improvement of living environment compared to the past, the deepening of the national urbanization level, and cities becoming the first choice for people's living and life. Nonetheless, as the number of city dwellers continues to rise, the management and control of China's urban regional environment face increasingly difficult challenges. The constant production of domestic waste by urban residents has a significant impact on the urban environment. Therefore, government departments have issued corresponding urban regional environmental control measures to improve the appearance of the urban environment at the government level. Due to the enormous size of the city, however, urban regional environmental control affects every aspect of residents' lives. It has become extremely challenging to objectively, effectively, and precisely evaluate the impact of urban regional environmental control. On the basis of this issue, this paper seeks to develop an evaluation system employing data deep mining technology and computer and artificial intelligence-related technologies in order to achieve an objective evaluation of the effect of environmental management and control in urban areas and to provide a scientific foundation for the subsequent decision-making by government departments. Moreover, the model is highly robust and compatible with different levels of management and control regulations, even when the control effect of the corresponding control deployment is evaluated objectively. [ABSTRACT FROM AUTHOR]

Accurate warning information of potential fault risk in the distribution network is essential to the economic operation as well as the rational allocation of maintenance resources. In this paper, we propose a fault risk warning method for a distribution system based on an improved RelieF-Softmax algorithm. Firstly, four categories including 24 fault features of the distribution system are determined through data investigation and preprocessing. Considering the frequency of distribution system faults, and then their consequences, the risk classification method of the distribution system is presented. Secondly, the K-maxmin clustering algorithm is introduced to improve the random sampling process, and then an improved RelieF feature extraction method is proposed to determine the optimal feature subset with the strongest correlation and minimum redundancy. Finally, the loss function of Softmax is improved to cope with the influence of sample imbalance on the prediction accuracy. The optimal feature subset and Softmax classifier are applied to forewarn the fault risk in the distribution system. The 191-feeder power distribution system in south China is employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

For the search engine, error-input query is a common phenomenon. This paper uses web log as the training set for the query error checking. Through the n-gram language model that is trained by web log, the queries are analyzed and checked. Some features including query words and their number are introduced into the model. At the same time data smoothing algorithm is used to solve data sparseness problem. It will improve the overall accuracy of the n-gram model. The experimental results show that it is effective. [ABSTRACT FROM AUTHOR]

Good track geometry state ensures the safe operation of the railway passenger service and freight service. Railway transportation plays an important role in the Chinese economic and social development. This paper studies track irregularity standard deviation time series data and focuses on the characteristics and trend changes of track state by applying clustering analysis. Linear recursive model and linear-ARMA model based on wavelet decomposition reconstruction are proposed, and all they offer supports for the safe management of railway transportation. [ABSTRACT FROM AUTHOR]