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Until today, In the daily work of dental radiologists, the flipping and identification of the intraoral x-ray images must be controlled by humans. No software or artificial design software can be improved, which causes the radiologist to take time and flip the teeth and put them into the correct tooth area. Therefore, under the increasing workload, how to shorten the time spent by the radiologist in handling is particularly important. In this study, through the Convolutional Neural Network (CNN) architecture in Deep Learning, the most commonly used intraoral films (Periapical, Horizontal Bite Wing, Vertical BiteWing) were inverted and identified to show the tooth area, and 16 tooth positions were designed for AI identification and learning; a total of 15,752 dental x-ray films (including original images, inverted 90°, 180° and 270° each 3938) for AI training. Therefore, in this study, 328 tests were performed after AI training (testing 1. tooth position recognition 2. flipping 90°, 180°, 270° image recognition each time), the recognition success rate is: 1. tooth position identification the success rate was 97.56%. 2. The average success rate of flip image recognition was 97.56%. Therefore, there is a great relationship between the number of AI learning images and the classification of image features and the success rate of recognition. In this study, the AI has been linked to the browser, and it can be used for teaching and research in the industry, research reference, etc., and is expected to be used clinically to reduce the workload of dental radiologists and dentists.