[Objective] The concentration of literature reading is mainly evaluated by manual methods or eye-tracking techniques. This paper uses computer vision technology to automatically detect and receive real-time feedback from the concentration evaluation, which also improves the application of intelligent technology in smart knowledge service. [Methods] First, we detected the head postures of the readers by their vertical and horizontal rotation angles. Then, we scored their fatigue and emotion with the closing eyes or yawning status. Third, we decided the readers’ sentiment based on these expression recognition results. Fourth, we applied the fuzzy comprehensive evaluation algorithm to determine the weight of relevant factors. Finally, we integrated the scores to obtain the reader’s concentration status at different reading processes. [Results] We applied the new model to the actual reading scenes to evaluate the reading concentration of head tilt, fatigue, and negative emotion, and the results were 26.3%, 25.2%, and 6.8% lower than the normal state, respectively. [Limitations] When the literature reading video showed blurred facial features, the detection accuracy was unsatisfactory, which needs improvement. There are also some extreme reading instances to be optimized. [Conclusions] The proposed model can adjust reading strategies and help libraries optimize collection development strategies.
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