Game User Preference Data Analysis and Market Guidance Based on Dynamic Attention GRU

With the development of Internet technology and social model, game products have become an important product people’s life for entertainment recreation, precise marketing has a winning means enterprises to improve competitiveness reduce labor cost consumption, major companies are also paying more attention data-based model. How dig out effective information from existing market behavior data is powerful implement marketing. Achieving positioning gaming guarantee innovative companies. For research on above problem, based SEMAS process mining, this paper proposes mining model recurrent neural network, which named as Dynamic Attention GRU (DAGRU) with multiple dynamic mechanisms, evaluates it two self-built sets user samples. The results demonstrate that method can effectively analyze predict player goals. system indeed provide accurate automated services, greatly reduces investment under traditional achieves targeting services certain application value.