Automatic recognition technology is one of the representative technologies of the Fourth Industrial Revolution and is currently used in various fields. RFID and NFC systems that we know are also being used by applying automatic recognition technology. RFID systems have evolved to replace existing barcode systems in a variety of areas that can be utilized as automatic recognition technologies. The biggest problem with today's RFID-enabled auto-recognition systems is the conflict between the leader and the tag that occurs when sharing the radio spectrum. Collision occurs when a tag within the recognition distance sends a signal to the reader simultaneously. If multiple tags are present in the RFID system and want to be recognized at the same time, the recognition performance of the tags will be significantly reduced and the recognition system will be able to recognize them. Causes performance problems. In other words, collision arbitration is aimed at fast and reliable recognition between RFID readers and tags, so it must be done without fail. It's a necessary skill. Existing studies use a variety of complex multi-connection technologies and collision prevention algorithms to mediate conflicts between tags. The anti-collision algorithm between leader and tag proposed in this paper used mechanical learning used in artificial intelligence. The problem with the existing Anti-Collision algorithm is extracted by learning the appropriate number of frames to mediate tag conflicts using instructional learning. Performance was compared with existing algorithms. We would like to study how to improve the leader recognition rate by using collision prevention algorithm using mechanical learning in recognition between tags and readers.