Abstract: Disclosed are a method, device, equipment and medium for determining customer tads based on deep learning. The method comprises the following steps: acquiring a conversation content between a customer and a robot customer service, inputting the conversation content into a preset multi-factor intent classifier to obtain a recognition result of product purchase intention output by the preset multi-factor intent classifier, setting a customer tab for the customer according to the recognition result of product purchase intention, and determining whether to provide manual service for the customer; acquiring a result of the manual service and conversation data of the customer in the manual service if the manual service is provided for the customer; and updating the customer tab of the customer according to the result of the manual service, and updating the preset multi-factor intent classifier according to the conversation data of the customer.

Abstract: A method and system for classifying input data arrived one by one in time, is provided including: a) respectively training a group of classifiers with a predetermined number with recent or previous input data whose real classes are obtained as learning samples, wherein a number of the recent input data are increased progressively in reverse chronological order; b) selecting the classifier having the highest accuracy on the recent input data from the group of classifiers based on recent classifying results of the group of classifiers; and c) classifying current input data using the selected classifier.