The public's acceptance level of recycled water use is a key factor that affects the popularization of this technology; therefore, it is critical to know the public's attitude in order to make guiding policies effectively and scientifically. To examine the major focuses and hot topics among the public about recycled water use, one of the major platforms for social opinion in China, the micro blog, is used as a source to obtain data related to the topic. Through the "follow-be followed" and "forward-dialogue" behaviors, a network of discussion of recycled water use among micro-blog users has been constructed. Improved particle swarm optimization has been used to allow deep digging for key words. Ultimately, key words about the topic of have been clustered into three categories, namely, the popularization status of recycled water use, the main application, and the public's attitude. The conclusion accurately describes the concerns of Chinese citizens regarding recycled water use, and has important significance for the popularization of this technology.

Pro-environmental behaviors related to reclaimed water reuse are regarded as important motivations for both environmental protection and the use of reclaimed water, and these motivations could affect the citizens’ decision whether they will accept reclaimed water reuse. A hypothesis model was developed as the NAM (Norm Activation Model) has changed, and this hypothesis model was used to explore the factors that affect the citizen’s decision about the reclaimed water reuse, and obtain a better understanding of the mechanism of urban citizens in environmental protection and the related outcomes. First, 584 samples were used to verify the reliability and validity of data, and AMOS21.0 was used to test the goodness-of-fit between the sample data and the hypothesis model. Based on this, the applicability of the improved NAM was verified through the study of recycled water reuse. The hypothesis model was used to analyze its direct influences, showing that environmental motivation has positive influences on the citizens’ acceptance toward recycled water reuse. Besides, Bootstrap method was used to verify the mediation effect, proving that awareness of consequences regarding environmental pollution caused by human activities and ascription of responsibility could strengthen the citizens’ motivation to protect the environment.

Reuse of recycled water is very important to both the environment and economy, while the public cognition degree towards recycled water reuse also plays a key role in this process, and it determines the acceptance degree of the public towards recycled water reuse. Under the background of the big data, the Hadoop platform was used to collect and save data about the public’s cognition towards recycled water in one city and the BP neural network algorithm was used to construct an evaluation model that could affect the public’s cognition level. The public’s risk perception, subjective norm, and perceived behavioral control regarding recycled water reuse were selected as key factors. Based on a multivariate clustering algorithm, MATLAB software was used to make real testing on massive effective data and assumption models, so as to analyze the proportion of three evaluation factors and understand the simulation parameter scope of the cognition degree of different groups of citizens. Lastly, several suggestions were proposed to improve the public’s cognition on recycled water reuse based on the big data in terms of policy mechanism.

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