With the rapid development of artificial intelligence models, the reasonable development and utilization of these models to serve people’s daily lives have become increasingly important. Given that traditional recommendation systems are unable to perceive users’ real-time emotions and therefore struggle to provide emotion-aligned book recommendations, this study proposes an intelligent book recommendation agent that integrates facial expression recognition technology with artificial intelligence models. In order to make the expression recognition results more accurate, this paper innovatively uses MTCNN-MobileNet_V2 fusion architecture for training. The training results show that the average accuracy of this method is about 23.6% higher than that of the traditional HOG based recognition method. The agent is based on the Raspberry Pi 4B hardware platform. Firstly, the user’s face image is collected in real time through Camera V2, and the face region is detected and aligned using MTCNN algorithm. Then, the expression classification is performed using MobileNet_V2 lightweight convolutional neural network; Then, the system integrates the large language model Qwen-3-32B to perform semantic reasoning and generate personalized book recommendations; Finally, MQTT and Home Assistant are used to realize the real-time display of recommendation results. The experimental results show that the average accuracy of this system is 94.72%, and the accuracy of emotion recognition is 97.7% under standard light (≈800 Lux); 83.1% under low illumination (≈350 lux). In the five person scene, the recognition accuracy is about 92%, the response delay is about 5 seconds, and the system response delay is moderate, which can realize real-time recommendation. The agent significantly improves the recommendation accuracy and user experience, and verifies the effectiveness of this method, which provides an effective basis for personalized recommendation based on emotional input in the future.