The invention discloses a product performance degradation interval prediction method based on support vector machine (SVM) and fuzzy information granulation, comprising the following steps of: step 1, collecting product multi-parameter performance degradation data; step 2, analyzing principal components of the multi-parameter degradation data; step 3, executing fuzzy information granulation on the obtained principal component data; step 4, executing SVM modeling for the granulated data; step 5, executing interval prediction on product performance degradation trends. In the method, a fuzzy information granulation method and a SVM method are combined, the interval prediction method for the product performance degradation trends is presented for the first time, and the problem of prediction on the degradation trends of performance state and the changing space in the product running process is solved. The method solves the problem of evaluation and prediction under a condition that a plurality of output performance characteristic parameters of some products with complex structures concurrently degrade, based on the principal component analysis method.