Purpose:This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Design/methodology/approach:A variety of methods such as the model construction,system analysis and experiments are used.The author has improved Morris’crossmapping technique and developed a technique for directly describing,visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.Findings:The visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers.It can reveal more and in-depth information than analyzing co-occurrence relations between two entities.Therefore,this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.Research limitations:The technique could only be used to analyze co-occurrence relations of less than three entities at present.Practical implications:This research has expanded the study scope of co-occurrence analysis.The research result has provided a theoretical support for co-occurrence analysis.Originality/value:There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles.This research defines multiple co-occurrence and the research scope,develops the visualization analysis tool and designs the analysis model of the knowledge discovery method. Purpose: This paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles. Design / methodology / machinery: A variety of methods such as the model construction, system analysis and experiments are used.The author has improved Morris’crossmapping technique and developed a technique for directly describing, visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles. Findings: The visualization tools and the knowledge discovery method can effectively reveal the multiple co-occurrence relations among three entities in collections of journal papers. It can reveal more and in-depth information than analyzing co-occurrence relations between two entities.Therefore, this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way. Research limitations: The technique could only be used to analyze co-occurrence relations of less than three entities at present. Practical implications: This research has expanded the study scope of co-occurrence analysis. The research result has provided an theoretical support for co-occurrence analysis. Originality / value: There has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. This research defines multiple co-occurrence and the research scope, develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.