With the introduction of a large number of malicious vulnerabilities in open source software, traditional vulnerability mining methods have a large number of invalid and lack of targeted test cases, resulting in low accuracy of vulnerability mining and high false alarm rate. In view of the above problems, this paper proposes an open source software vulnerability mining model based on improved fuzzy test combined with seeker optimization algorithm (Fuzz-SOA). Firstly, the vulnerability POC file is executed in the target open source software, and the effective fragment of the taint data propagation is obtained by the dynamic taint analysis method as the effective seed, and then the effective seed is screened. Then, the comprehensive seed evaluation function in the SOA intelligent algorithm is improved from the five evaluation types of code coverage, number of crashes, execution time, seed size, and number of hash values of the seed execution path, and the global optimal seed is obtained. In order to increase the randomness and diversity of the optimal seed, the seed mutation is carried out by combining the random mutation method, and the mutated seed is used as the input use case of the fuzzy test of the target open source software. The results of the comparison model show that the model has better open source software vulnerability mining capabilities and solves the problems existing in traditional methods.