With the development of big data technology and the widespread application of artificial intelligence, predicting user satisfaction has become a key method for operators to address poor signals and network quality issues, reduce customer complaints, and gain competitive advantages. This paper proposes a user satisfaction prediction algorithm that integrates machine learning techniques, building a high-accuracy prediction model through analyzing user behavior data and feedback. The model has demonstrated superior predictive accuracy and good generalizability across multiple datasets through experimental validation. The implementation of this algorithm is significant for understanding user needs and improving service quality.