Knowledge graph is a graph-based structural representation of knowledge. One of the key problems about knowledge graph in both research and practice is how to construct large-scale high-quality knowledge graphs. This paper presents an approach to construct knowledge graphs based on Internet-based human collective intelligence. The core of this approach is a continuously executing loop, called the EIF loop or EIFL, consisting of three activities: free exploration, automatic integration, and proactive feedback. In free exploration activity, each participant tries to construct an individual knowledge graph alone. In automatic integration activity, all participants’ current individual knowledge graphs are integrated in real-time into a collective knowledge graph. In proactive feedback activity, each participant is provided with personalized feedback information from the current collective knowledge graph, in order to improve the participant’s efficiency of constructing an individual knowledge graph. In particular, a hierarchical knowledge graph representation mechanism is proposed, a knowledge graph merging algorithm is designed driven by the goal of minimizing the collective knowledge graph’s general entropy, and two ways for context-dependent and context-independent information feedback are introduced, repectively. In order to investigate the feasibility of the proposed approach, three kinds of experiment are designed and carried out: (1) the merging experiment on simulated graphs with structural information only; (2) the merging experiment on real large-scaled knowledge graphs; (3) the construction experiment of knowledge graphs with different number of participants. The experimental results show that: (1) the proposed knowledge graph merging algorithm can find high-quality merging solutions of knowledge graphs by utilizing both structural information of knowledge graphs and semantic information of elements in knowledge graphs; (2) EIFL-based collective collaboration improves both the efficiency of participants in constructing individual knowledge graphs and the scale of the collective knowledge graph merged from individual knowledge graphs, and shows sound scalability with respect to the number of participants in knowledge graph construction.