SELF-LEARNING ONLINE UPDATE METHOD AND SYSTEM FOR MULTI-CLASSIFICATION MODEL, AND APPARATUS

摘要

Disclosed is a self-learning online update method for a multi-classification model, relating to artificial intelligence. The method comprises: according to a preset statistical period, performing monitoring and compiling statistics on the prediction performance of a model to be updated, and storing, in a statistical database, a predication performance statistical result in each statistical period (S110); checking data in the statistical database by using a preset trigger mechanism, so as to determine whether said model needs to be updated online (S120); if said model needs to be updated online, acquiring online newly generated data, and updating training data of said model according to the newly generated data (S130); and updating and training said model by using the updated training data, so as to obtain an updated multi-classification model (S140). The present application further relates to blockchain technology. The statistical database is stored in a blockchain. The existing problems of the prediction precision of a multi-classification model being significantly reduced as time goes by, and the multi-classification model being unable to be automatically updated can be solved.