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[Purpose/Significance] Evidence-informed decision-making is a means to bridge the gap between research and policy and evidence synthesis has become an important tool for evidence-based decision-making in many fields. However, evidence synthesis is resource-intensive, especially when it comes to scientific knowledge on complex issues. The efficiency of evidence synthesis currently cannot meet the needs of decision makers. Crowdsourcing is seen as a potential way to improve the productivity of evidence synthesis. At present, the research and practice on the applications of crowdsourcing in evidence synthesis is still in its infancy. This study takes the application of crowdsourcing in the Cochrane Crowd citizen science project as an example to summarize the practical applications of crowdsourcing in evidence synthesis. The comprehensive analysis of the application mechanism of crowdsourcing in Cochrane Crowd project will provide certain reference and inspiration for the use of crowdsourcing in evidence synthesis, so as to improve the production efficiency of evidence synthesis and provide timely and powerful scientific information for evidence-based decision-making. [Method/Process] The application mechanism of crowdsourcing in the Cochrane Crowd citizen science project was analyzed from five dimensions: crowdsourcer, volunteers, crowdsourcing task, Cochrane Crowd platform and effectiveness evaluation, using literature research, network investigation, case analysis and other methods. Cochrane Crowd provides an easy-to-use interface for contributors to engage volunteers to participate and design , in addition to task-focused learning activities, diverse ways of accessing tasks, interactive online training modules and feedback mechanisms to improve the likelihood of volunteers' performing tasks correctly. At the same time, an agreement algorithm is provided at the platform level to aggregate the crowd classification results, which further improves the possibility of correct classification of records. In addition, the platform has used the records identified by the crowd to build a machine-learning model called as RCT classifier which can predict how likely a new citation is to be described an RCT to reduce the manual burden. [Results/Conclusions] Crowdsourcing is an effective method to improve the efficiency of evidence synthesis and shorten the production cycle. With comprehensive participant training and appropriate quality control mechanisms, it is possible to produce high quality crowdsourcing results that meet the "gold standard" of evidence synthesis. In order to motivate volunteers to participate and promote continued engagement, participants are suggested to be provided with clear goals, clear tasks, and timely feedback or rewards. Interest and activity in introducing crowdsourcing into evidence synthesis is growing rapidly, and new tools and platforms to facilitate crowdsourcing also need to be further developed as researchers from different disciplines use crowdsourcing in the evidence synthesis projects. In the future, the application of crowdsourcing in evidence synthesis in different fields and in different stages of evidence synthesis should be further studied.