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The accuracy maintenance and operation reliability of the hydrostatic turntable are associated with many factors whose influence on bearing load carrying capacity is the key question of researching the hydrostatic turntable. In this study, an intelligent hydrostatic turntable monitoring system is proposed based on multi-source data acquisition. Firstly, the timely main parameter variations are acquired through different kinds of sensors installed on the turntable to form an input data aggregation. Then, by analyzing the static, dynamic, and thermal characteristics of the turntable using Finite Difference Method (FDM) and Runge–Kutta method (RKM), a set of equations for the bearing performance evaluation is established. Computational results are converted to the motor signal for the pump for controlling oil supply rate. Finally, the load-carry supporting performance of each oil pad is controlled precisely and individually by a pump with multiple outputs driven by servo motors. An intelligent input, processing, and output (IPO) real-time system of the hydrostatic turntable is established to monitor its service performance. Finally, the accuracy compensation by data statistics and reliability evaluation by abnormal data detection are carried out to improve the working performance of hydrostatic turntable.