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However, due to the limitation of characterization technology on a space\u2212time scale, the existing technologies cannot provide real\u2010time in situ tracking of entire dynamic processes of fibrous matters. To address this shortcoming, an artificial intelligence\u2010based codebase, named the artificial intelligence\u2010based fiber motion tracking (AI\u2010FMT), is proposed for analyzing, studying, and predicting motion behaviors of fibrous matters. The proposed AI\u2010FMT can automatically extract morphology information of fibrous matters from a large number of pictures or video streams, which serve as an input for machine learning to predict the motion pattern of fibrous matters. Using a finite\u2010element concept in understanding the point\u2212coordinate relationship, the AI\u2010FMT greatly simplifies the architecture of the neural network during motion prediction. For the case of 928 trainable parameters, an average accuracy of 97.8% is achieved in predicting variations in morphological parameters, such as mean square radius of gyration, end\u2010to\u2010end distance, and curvature in the movement process of animal hair. 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