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It plays an important role in improving the quality reliability, process stability and production efficiency of microwave components that rapid and accurate positioning of the quality problems in the microwave components production process. Based on the traditional manual analysis logic of microwave components process quality problems, through the mining and analysis of the data characteristics of every link in the current microwave components production process, the modeling method of production big data and failure analysis knowledge fusion is put forward, and it is applied to the auxiliary troubleshooting of process problems. Firstly, based on the characteristics of microwave components process quality data, the key fault data is obtained through data cleaning, which serves as the basic data of big data mining modeling. Secondly, from the perspective of quality feature similarity of microwave components, cluster different microwave components to improve the information density of sparse data. Finally, the big data mining algorithm is used to fuse the prior knowledge of failure analysis to build the knowledge model for auxiliary troubleshooting. Based on the sample data, the case analysis and the software deployment of the model are carried out for the proposed modeling method, which verifies the feasibility of the application in the analysis of microwave component process quality problems.