With the rapid development of digital and intelligent e-commerce, the live streaming e-commerce industry is facing multiple challenges such as fragmented user demands, intensified market competition, and insufficient marketing efficiency. Based on user behavior data, this study constructs an extended RFM-QPC-V user characteristic model, and uses the K-Means clustering algorithm to generate user portraits, accurately depicting users' consumption preferences and value hierarchies. On this basis, a gradient boosting model is adopted to design a dynamic voucher delivery strategy, and the model performance is optimized through the SMOTE oversampling technique and the ENN under-sampling method. Experimental results show that the recall rate of this model reaches 0.7 and the AUC-ROC value reaches 0.88. It performs excellently in accurately identifying high value users and improving the conversion rate, providing effective technical support and decision - making reference for live streaming e-commerce. In addition, this study proposes a layered distribution and regional marketing strategy, designing differentiated coupon schemes for different customer groups, aiming to empower the efficient development of the smart digital e-commerce industry.