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.

A stereo matching algorithm based on probability assignment is proposed to meet the real-time demand of stereo matching using SURF(Speeded Up Robust Features). In order to accelerate search of matching points, probability assignment of feature points is acquired based on the correlation between fuzzy clustering and probability assignment, as well as the necessary conditions of information entropy; then, when extracting ORB feature points, a data set is used to record the sequence of feature points after decreasing the edge effect; epipolar line, uniqueness and sequence constraints narrow down the search space; the Hamming distance between descriptors are calculated and the nearest neighbor matching becomes the stereo matching result. Experimental results show that the proposed algorithm accelerates matching and maintains high accuracy, even when applied to non-standard pictures.