As a method of automatically detecting application vulnerabilities, fuzzing often serves for various software and computer systems. The quality of the seed file is very important to the fuzzing test. Therefore, this study proposes a method for generating fuzzing seed input based on machine learning. It relies on sample input and machine learning-based technology to learn the rules and grammar of sample input, which are then used to generate new seed input. We also propose a sampling method, considerably improving the coverage of the new seed input.