Abstract: In today’s era of big data, it is difficult for traditional naive Bayesian algorithms to efficiently and accurately deal with the complexity and uncertainty of big data. Based on the traditional Naive Bayes algorithm, this paper proposes an efficient and accurate quantum fuzzy Bayesian classification algorithm. First, the “fuzzy set theory + naive Bayes theory” is cross-integrated, the fuzzy prior probability and fuzzy conditional probability are defined, and the naive Bayes is extended to fuzzy naive Bayes to construct a fuzzy Bayes model; Secondly, a quantum fuzzy naive Bayesian classification algorithm is investigated and implemented by quantizing fuzzy data sets (encoding to quantum states) and designing quantum circuits. Finally, the algorithm proposed in this paper is applied to the iris dataset. Simulation experiments show that the proposed classification algorithm has higher classification efficiency and accuracy compared with the traditional Naive Bayesian classification algorithm.