The exploration of facial emotion recognition aims to analyze psychological characteristics of juveniles involved in crimes and promote the application of deep learning to psychological feature extraction. First, the relationship between facial emotion recognition and psychological characteristics is discussed. On this basis, a facial emotion recognition model is constructed by increasing the layers of the convolutional neural network (CNN) and integrating CNN with several neural networks such as VGGNet, AlexNet, and LeNet-5. Second, based on the feature fusion, an optimized Central Local Binary Pattern (CLBP) algorithm is introduced into the CNN to construct a CNN-CLBP algorithm for facial emotion recognition. Finally, the validity analysis is conducted on the algorithm after the preprocessing of face images and the optimization of relevant parameters. Compared with other methods, the CNN-CLBP algorithm has higher accuracy in facial expression recognition, with an average recognition rate of 88.16%. Besides, the recognition accuracy of this algorithm is improved by image preprocessing and parameter optimization, and there is no poor-fitting. Moreover, the CNN-CLBP algorithm can recognize 97% of the happy expressions and surprised expressions, but the misidentification rate of sad expressions is 22.54%. The research result provides data reference and direction for analyzing psychological characteristics of juveniles involved in crimes.

In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

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