In recent years, the field of deep learning has flourished, not only breaking through many difficult problems that are difficult to be solved by traditional algorithms but also bursting with greater vitality when combined with other fields. For example, product emotional design based on deep learning can integrate users' emotional needs into the actual product design. In this paper, we aim to use deep learning and affective technology in the creation of AR interactive picture books to transform the reading process from static to dynamic, enrich visual stimulation, and increase the fun and interactivity of reading. In this paper, based on the three-level theoretical model of emotion, the emotion labeling results are input to a deep neural network for learning, to establish an emotion-based recognition model for picture book images. The results show that the model can well analyze the emotion of images in AR picture books, and the accuracy of prediction is a big improvement compared with traditional machine recognition algorithms. The application of AR virtual implantation technology in interactive picture books on the market is often just a marketing gimmick while combining deep learning and emotional technology can better create diverse interactive picture books to meet children's emotional reading needs, enhance reading engagement, and stimulate children's creativity.

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STEM education is a hot issue in modern education, and it is important to study whether middle school students enter STEM careers in the future in the early stage of career planning. In this paper, we collected students’ behavioral data through the online tutoring platform ASSISTments, divided the raw log data into five types: single-valued, binary-valued, multi-valued, continuous-valued and cumulative, and aggregated them using different data reconstruction methods. Then, a width & depth prediction model based on feature crossover is proposed to perform feature crossover on the aggregated data, and then the depth and width models are jointly trained using. During the training process, the AUC of the FC-Wide&Deep model improved rapidly from 0.800 to 0.845 in the 1st to 16th training rounds, and then slowly climbed with the increase of training rounds. By averaging the results of the three tests, the AUC index of the FC-Wide&Deep model test results improved by 1.29% compared to the DNN model, and the RMSE index improved by 2.08% compared to the BSN-FM model. The FC-Wide&Deep model is generalizable and generalizable, and can be applied to predict whether students will enter STEM careers in the future, thus contributing to the cultivation and leadership of STEM talents in the field of education.