The invention relates to the technical field of intelligent product recommendation, in particular to an intelligent product recommendation method based on a
large model, which comprises the following steps of: firstly, receiving a user dominant demand text and real-time behavior data, analyzing the behavior data through a dynamic window self-adaptive mechanism, identifying a
time sequence correction logic chain for adding, comparing clicking and canceling adding, and finally recommending the user dominant demand text to the real-time behavior data. Combining attribute-level focus capture and visual thermodynamic diagram feedback positioning core attributes, quantifying a behavior weight offset effect, generating a composite
label fusing explicit and implicit demands, adopting time-space density clustering statistics attributes to compare effective click frequencies, combining an attribute dependency relationship network to rearrange attribute priorities, and obtaining a real-time feature; according to the method, dynamic
attribute weight distribution is formed by differentially distributing weights through a weight rebalance tree model, finally, three types of information are integrated through a
large model, the weights are calibrated through
antagonism verification, a product recommendation
list conforming to real preferences of users is generated, and recommendation accuracy and individuation degree are improved.