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In the application of rotating machinery, inner ring failure, outer ring failure and rolling element failure are easy to occur, which also directly affects the service life of mechanical parts. In order to accurately diagnose the fault behavior of equipment components and extend the life level of rotating machinery equipment, the application of neighborhood knowledge graph algorithm in fault diagnosis of rotating machinery equipment is studied. The function expression of the neighborhood knowledge graph algorithm is solved, and based on this, the fault data is recommended, and the deep mining of the rotating machinery equipment fault data is realized by the way of preprocessing. The key fault data is fused and dimensionally reduced, and the specific fault diagnosis process is improved according to the definition conditions of kernel characteristics, and the fault diagnosis algorithm of rotating machinery equipment based on neighborhood knowledge graph algorithm is designed. The experimental results show that the application of the above method can accurately diagnose the inner ring fault, the outer ring fault and the rolling element fault. The purpose of prolongating the service life of the rotating machinery equipment can be achieved by dealing with the diagnosed fault behavior appropriately.