Effective search over knowledge graphs can provide support for applications such as question answering and semantic search. However, when the user cannot give a clear query, accurately capturing the user’s interest and finding the answer are difficult for machines. Hybrid human-machine active search provides a pathway to bridge the gap between users and machines. Hybrid human-machine active search is a kind of interactive search, and it is originated from the thought of active learning in machine learning field. The core idea is to let the machine issue questions to the user, to obtain information from the user feedback, and then based on this information to eventually capture user intent and return answers. In this paper, we stand on recent advances in knowledge graph representation learning techniques and propose a hybrid human-machine active search in the vector space of a knowledge graph. Specifically, the knowledge graph is first embedded into the low-dimensional vector space, which quantizes the characteristics of entities and relationships, and at the same time, the user’s interests and preferences are embedded into the same space. Then, the machine actively proposes questions to the user, and gets the feedback information by asking the user to rate the specific entity, thus updating the user preference positioning in the vector space. We design an evaluation method to measure the user’s interest in a specific entity based on the Euclidean distance between the preference point and other entities, and finally find the final target entity to return to the user after multiple turns of human-machine interaction. In the experiment part, we conduct experiments on the knowledge graph embedding and the active search respectively, and the experimental results show that the proposed method is effective.