The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. Numerical transformations do not accurately and comprehensively represent expert decision-making information expressed in natural language. This paper presents a automatic decision-making for algal bloom management based on knowledge graph. A general framework of decision-making management is constructed for the algal bloom management process. Secondly, the expert decision-making knowledge graph is constructed and visualization is carried out using the Neo4j graph database. Then considering the small amount of data and the brief text structure of the expert decision-making information, the joint extraction of entities and relations model which Bert-GRU-Attention-CRF. Finally, real-time water quality data and expert decision-making recommendations are fused. The model proposed in this paper works better verified by experiments. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms.