This study explores the application of emotion recognition technology to enhance user experience in real-time, focusing on its integration into online learning platforms. The research employs convolutional neural networks (CNN) and long short-term memory networks (LSTM) to analyze facial expressions and voice emotions, enabling dynamic adjustments to learning content based on users' emotional states. An experimental group using an emotion recognition system showed significant improvements in task completion time, error rates, and quiz scores compared to a control group using a traditional platform. The experimental group also reported higher satisfaction levels, indicating that real-time emotional monitoring and adaptive content adjustments enhance user engagement and learning outcomes. The study highlights the potential of emotion recognition technology to improve user experiences across various domains, including online education, healthcare, customer service, and entertainment. However, challenges such as multimodal data integration, real-time processing capabilities, and privacy concerns remain. The research demonstrates that emotion recognition can provide personalized and adaptive interactions, significantly improving user satisfaction and effectiveness in learning environments. The findings suggest that integrating emotion recognition into user interfaces can lead to more intuitive and engaging experiences, offering valuable insights for future research and practical applications. The study also emphasizes the importance of addressing technical and ethical challenges to ensure the successful implementation and widespread adoption of emotion recognition technology.This study explores the application of emotion recognition technology to enhance user experience in real-time, focusing on its integration into online learning platforms. The research employs convolutional neural networks (CNN) and long short-term memory networks (LSTM) to analyze facial expressions and voice emotions, enabling dynamic adjustments to learning content based on users' emotional states. An experimental group using an emotion recognition system showed significant improvements in task completion time, error rates, and quiz scores compared to a control group using a traditional platform. The experimental group also reported higher satisfaction levels, indicating that real-time emotional monitoring and adaptive content adjustments enhance user engagement and learning outcomes. The study highlights the potential of emotion recognition technology to improve user experiences across various domains, including online education, healthcare, customer service, and entertainment. However, challenges such as multimodal data integration, real-time processing capabilities, and privacy concerns remain. The research demonstrates that emotion recognition can provide personalized and adaptive interactions, significantly improving user satisfaction and effectiveness in learning environments. The findings suggest that integrating emotion recognition into user interfaces can lead to more intuitive and engaging experiences, offering valuable insights for future research and practical applications. The study also emphasizes the importance of addressing technical and ethical challenges to ensure the successful implementation and widespread adoption of emotion recognition technology.