There are many factors that can affect the calciner process of cement production, such as highly nonlinearity and time-lag, making it very difficult to establish an accurate model of the cement precalciner kiln (PCK) system. In order to reduce transport energy consumption and to ensure the quality of cement clinker burning, one needs to explore different control methods from the traditional way. Adaptive Critic Design (ACD) integrated neural network, reinforcement learning and dynamic programming techniques, is a new optimal method. As the PCK system parameters change frequently with high real-time property, ADACD (Action-Dependant ACD) algorithm is used in PCK system to control the temperature of furnace export and oxygen content of exhaust. ADACD does not depend on the system model, it may use historical data to train a controller offline, and then adapt online. Also the BP network of artificial neural network is used to accomplish the network modeling, and action and critic modules of the algorithm. The results of simulation show that, after the fluctuations in the early control period, the controlled parameters tend to be stabilized guaranteeing the quality of cement clinker calcining.