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The purpose of this paper is to investigate consumers' purchase intention of wild freshwater fish. Facing the endangering ecology in the Yangtze River Basin, the Chinese government has implemented a ten-year fishing ban policy to protect the wild freshwater fishery resources from 2020. In this context, such questions are raised as how do consumers react to this and are they willing to reduce or even refuse to purchase wild freshwater fish to protect the aquatic biological resources in the Yangtze River Basin?

A total of 1,235 consumers from eight provinces (including two province-level municipalities) in the Yangtze River Basin filled out the online questionnaires. The data (n = 1,096) are analyzed by structural equation model (SEM) to verify the relationships between the variables.

The results show that subjective norm is the strongest direct determinant of purchase intention, followed by personal norm, attitude, environmental concern and perceived behavioral control. It is also found that attitude, subjective norm, perceived behavioral control and environmental concern have significant effects on personal norm which plays a significant mediating role in forming purchase intention. On this basis, specific policy recommendations are proposed.

This paper investigates consumers' purchase intention from the perspective of ecological protection and obtains a more comprehensive explanation of the purchase intention by combining the theory of planned behavior (TPB) and theory of norm activation.

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.