Abstract: The observation feature dimension of resistance variant fault diagnosis in mine ventilation is high, and there are many irrelevant and redundant features. A multi-objective optimization model of mine ventilation resistance variant fault observation feature selection was established by taking the minimum coverage of observation points and cross-validation error as the goal. The k-nearest neighbor algorithm was used as the solution method for the fault diagnosis model. A multi-objective optimization feature selection method of resistance variant fault observation based on non-dominated sorting genetic algorithm Ⅱ was proposed. The critical problems are solved by deploying observation points only at necessary locations to reduce the cost of resistance variant fault diagnosis, eliminating redundant or irrelevant features between observation features to improve the performance of resistance-variant fault diagnosis learners, and alleviating the occurrence of over-fitting or under-fitting to enhance the model's generalization ability. Seven feature schemes were used to carry out the observational feature combination selection and comparison experiments and a sample-free model for verification experiments. The results show that after the relative observation point coverage falls to 0.2-0.5, the rate of fault diagnosis accuracy is converged to a stable state. A single feature type obtains the lowest accuracy rate in all comparison tests. Under the condition of ensuring fault diagnosis performance, the optimized selection of observation features dramatically reduces the complexity of the resistive fault diagnosis model. In the sample-free model, the optimized selected observation features have the same effectiveness as the supervised model. The proposed method can effectively eliminate redundant or irrelevant features, improving the fault diagnosis model′s performance. The proposed method can provide a new theoretical approach for real-time network calculation. The optimization of the locations of a small number of observation points provides technical support for the realization of intelligent ventilation.