The assessment and diagnosis of agricultural drought vulnerability is the basis for the scientific control and prevention of agricultural drought risk. In order to quantitatively evaluate the regional agricultural drought vulnerability and effectively deal with the fuzziness and randomness between evaluation sample and evaluation grade, the entropy fuzzy pattern recognition model, which was coupled with maximum information entropy principle and fuzzy pattern recognition method, was applied to the evaluation of agricultural drought vulnerability. Furthermore, in order to find methods to reduce drought vulnerability, the subtraction set pair potential method was used to further identify the main influence factors of agricultural drought vulnerability. In addition, a case study was carried out in Bengbu City, China. The results showed that during the period from 2001 to 2010, the evaluation value of agricultural drought vulnerability in Bengbu dropped from grade IV to III, which meant that the tolerance of agricultural system to drought had been improved. Moreover, the main factors that affected the vulnerability in Bengbu were precipitation, water use efficiency and irrigation protection area rate. And the latter two were the main objects of regulation and control. Compared with the other three methods, the physical concept of drought vulnerability assessment model based on entropy fuzzy pattern recognition is more obvious, and the evaluation results are reasonable and credible. The drought vulnerability diagnosis method based on subtraction set pair potential can accurately identify key influence factor and provide measures to reduce drought vulnerability and improve drought risk management.