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Envisioning the construction of navigation constellations combining high, medium, and low orbits for the next generation navigation systems, the exponential increase in the quantity and types of telemetry parameters poses challenges to traditional health evaluation methods. These challenges stem from the difficulty in applying past expert knowledge and the inadequacy of fault mechanism reservoirs for comprehensive coverage. To address this, a method for evaluating the health of navigation satellite payload subsystems based on Local Outlier Factor Detection and Bayesian Network Structure Learning is proposed. Experimentation involving the collection of data before and after actual fault occurrences in a satellite system validated the ability of the Local Outlier Factor Detection method to accurately output the health status at a coarse-grained level. Efficiency and accuracy of the Bayesian Network Structure Learning were analyzed and compared using three scoring functions. Experimental results indicated that when employing BDeuScore, K2Score, and BicScore as scoring functions, the learned models achieved respective accuracy levels of 87.4%, 80.5%, and 85.2% in assessing the system"s health. Limitations of the Local Outlier Factor Detection and Bayesian Network Structure Learning methods were summarized, providing new directions and insights for the health assessment of navigation satellite subsystems.