The invention discloses a shilling attack detection method based on a stack type sparse self-encoder. The shilling attack detection method is mainly used for solving the problem that corresponding characteristics of shilling attack users of different types need to be extracted in the prior art. The shilling attack detection method comprises the following steps: (1) inputting an initial scoring data set; (2) initializing the initial scoring data set; (3) directly using the score of each user as input to train the stack type sparse self-encoder, and extracting characteristic data of the user; (4) using the extracted characteristic data as input to train a Naive Bayes classifier; and (5) calculating the probability of a user with an unknown type belonging to each type according to the trained Naive Bayes classifier, and finding out the shilling attack user. The shilling attack detection method disclosed by the invention can be used for directly using the stack type sparse self-encoder to extract the characteristic data of each user, can be used for stably detecting shilling attack users of a plurality of types and can be used for detecting malicious attack users in an Internet system.