Optical kidney biopsy, serological examination, and clinical symptoms are the main methods for membranous nephropathy (MN) diagnosis. However, false positives and undetectable biochemical components in the results of optical inspections lead to unsatisfactory diagnostic sensitivity and pose obstacles to pathogenic mechanism analysis. In order to reveal detailed component information of immune complexes of MN, microscopic hyperspectral imaging technology is employed to establish a hyperspectral database of 68 patients with two types of MN. Based on the characteristic of the medical HSI, a novel framework of tensor patch-based discriminative linear regression (TDLR) is proposed for MN classification. Experimental results show that the classification accuracy of the proposed model for MN identification is 98.77%. The combination of tensor-based classifiers and hyperspectral data analysis provides new ideas for the research of kidney pathology, which has potential clinical value for the automatic diagnosis of MN. (c) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement With an incidence rate of more than 10%, chronic kidney disease (CKD) has become a global public health problem, which is the eighth leading cause of women death and affects approximately 195 million women worldwide [1]. If CKD is not treated in time, it may develop into end-stage kidney disease, which requires dialysis or kidney transplant to maintain life. Finding the cause of the disease and treating it early can usually prevent the deterioration of CKD, thereby improving the patient's quality of life. Among CKD, membranous nephropathy (MN) [2] is one of the most common pathological types of adult nephrotic syndrome. According to etiology, MN can be divided into primary MN (PMN) and secondary MN (SMN). The etiology of PMN has not been clarified, and SMN often secondary to tumors, lupus erythematosus, hepatitis B virus, autoimmune diseases, drug and poison exposure, etc [3]. Researches relating the differential