The smelting process of blast furnace is complex and changeable, many factors affect the conditions of blast furnace, and the operation process is complex. In order to ensure the stable and forward flow of the blast furnace condition, an abnormal detection model of blast furnace conditions based on principal component analysis (PCA) and statistical process control (SPC) has been developed. Due to the high coupling and non-Gaussian characteristics of blast furnace operating parameters, the model first used a principal component analysis algorithm to perform cluster analysis on the historical offline data of the actual production of the blast furnace. Then, the multivariate control chart based on T2 statistics and single value control chart were used to monitor the new variables and related parameters after clustering, so as to monitor abnormal furnace conditions in the blast furnace. The model can monitor the abnormal fluctuations of blast furnace conditions in real time. The PCA algorithm reduced the high-dimensional data of the blast furnace to low-dimensional, greatly removing noise and unimportant features in the data, saving a lot of cost and time in actual production and application. The ironmaking process of a certain blast furnace in Ma'anshan Iron and Steel was selected as the application scenario, and the algorithm was adjusted and improved in combination with the data characteristics,and the reliability of the model and the effectiveness of the algorithm were verified through historical data simulation and real-time online operation. The achievements in this paper make certain contributions to guiding the actual operation technology of the blast furnace.