Aiming at the problem that Protein-Protein Interaction (PPI) network functional module mining method based on spectral clustering and Fuzzy C-Means (FCM) clustering has low accuracy and low running efficiency, and is susceptible to false positive, a method for Functional Module mining in uncertain PPI network based on Fuzzy Spectral Clustering (FSC-FM) was proposed. Firstly, in order to overcome the effect of false positives, an uncertain PPI network was constructed, in which every protein-protein interaction was endowed with a existence probability measure by using edge aggregation coefficient. Secondly, based on edge aggregation coefficient and flow distance, the similarity calculation of spectral clustering was modified using Flow distance of Edge Clustering coefficient (FEC) strategy to overcome the sensitivity problem of the spectral clustering to the scaling parameters. Then the spectral clustering algorithm was used to preprocess the uncertain PPI network data, reducing the dimension of the data and improving the accuracy of clustering. Thirdly, Density-based Probability Center Selection (DPCS) strategy was designed to solve the problem that FCM algorithm was sensitive to the initial cluster center and clustering numbers, and the processed PPI data was clustered by using FCM algorithm to improve the running efficiency and sensitivity of the clustering. Finally, the mined functional module was filtered by Edge-Expected Density (EED) strategy. Experiments on yeast DIP dataset show that, compared with Detecting protein Complexes based on Uncertain graph model (DCU) algorithm, FSC-FM has F-measure increased by 27.92%, running efficiency increased by 27.92%; compared with an uncertain model-based approach for identifying Dynamic protein Complexes in Uncertain protein-protein interaction Networks (CDUN), Evolutionary Algorithm (EA) and Medical Gene or Protein Prediction Algorithm (MGPPA), FSC-FM also has higher F-measure and running efficiency. The experimental results show that FSC-FM is suitable for the functional module mining in the uncertain PPI network.