Abstract: It is of great significance to analyze and utilize the massive human, machine, thing and system data contained in industrial Internet to optimize the manufacturing system and service system covering the whole industrial chain and value chain. However, the processing and analysis of industrial Internet big data brings us opportunities at the same time, but also unprecedented privacy concerns. Privacy security is an important part of industrial Internet security. Research on big data analysis algorithms of industrial Internet with privacy protection has been very urgent and severe. Industrial Internet big data processing also has higher requirements for privacy, efficiency and accuracy. We propose a quantum K-nearest neighbor (KNN) algorithm with privacy-protecting characteristics, and find an encryption method for the original training sample set and the sample to be tested, so that the ciphertext sample input to the quantum cloud server can get the same prediction result as the original sample input. In this algorithm, because of the inversion of a prediction result corresponding to N+1 input data, it is difficult to deduce the model, parameters, input data and related attribute characteristics from the prediction results obtained by multiple visits to the quantum cloud server. Therefore, the proposed algorithm can well resist model extraction attack, model reverse attack, member inference attack, attribute inference attack and so on. Compared with the existing quantum machine learning algorithm with privacy protection, it is found that the privacy protection scheme in this paper is superior to the existing schemes in three aspects of privacy, complexity and availability, which achieves privacy protection without increasing additional computing overhead, reducing the efficiency and availability of the algorithm, and affecting the accuracy of the algorithm. We provide a new method to protect the privacy of quantum machine learning and a new idea to improve the comprehensive performance of industrial Internet big data analysis in terms of privacy, efficiency and accuracy, which has important theoretical value and practical significance.