Urban ecological environment investigation based on a cloud computing platform and optimization of computer neural network algorithm

The rapid development of Internet technology has contributed to the development of the Internet. The rapid expansion of the industrial application scale and the sharp increase of data generated by an industrial application and cloud computing have promoted the birth and development of cloud computing platform technology. Data analysis has become an important support for company decision-making; how to quickly and effectively extract useful information from large data sets is very important. Among them, clustering analysis and computer neural network algorithms are the most important methods of data mining. Due to the limitations of computer performance and programming models, traditional data mining methods cannot meet the efficiency and computing needs of a large amount of information. The development of cloud computing technology has opened up a new research field of cloud mining clustering analysis and neural network algorithm formation. The first research is the deployment of the Hadoop cluster based on Linux, and the novelty lies in the parallelization of the MapReduce cluster algorithm. Because there are many clustering algorithms, this paper starts with the K-means clustering algorithm and optimizes the earlier algorithm according to the MapReduce programming model. Through the text clustering process of wine data set in the UCI database, the parallel algorithm is applied to the Hadoop cloud computing platform. Experiments show that the parallel K-means clustering algorithm in MapReduce can significantly improve the execution speed. This paper applies it to the urban ecological environment survey in China, clarifies the relationship between various factors of urban environmental governance, expands the content of urban environmental governance in China, and improves the ecological environment of Chinese cities. © 2021, Saudi Society for Geosciences.