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Turbine engine is a kind of engine that burns fuel and uses high-speed flowing gas to generate power. In the state of high-speed rotation, its unbalanced vibration will lead to increased wear of turbine engine components and reduce service life. In order to effectively solve this problem, a fault diagnosis system for unbalance vibration of turbine engine in high-speed rotation state is designed. This design divides the system framework into three layers, including the lower computer layer, upper computer layer, and client layer. The system uses vibration sensors and sound sensors to collect status signals during the operation of the turbine engine under high-speed rotation, and converts the signals into signals that can be recognized by the lower computer through a transmitter. The lower computer runs the processing program, filters the signal, extracts fault features, and then forwards the signal to the upper computer through the Zigbee remote communication module. The upper computer layer runs a knowledge graph diagnostic program to construct a knowledge graph of engine imbalanced vibration faults, and combines it with Bayesian networks to infer fault types, calculate the probability of fault occurrence, and achieve high-precision imbalanced vibration fault diagnosis. The experimental results show that compared with the three traditional diagnostic methods, the ROC curve of the designed system is at the top, and the area below the curve is larger. The AUC value is 0.847, indicating that the designed system has strong fault diagnosis ability and can ensure the accuracy of diagnostic results.