This paper studies the look-ahead optimal control problem of the conveyor-serviced production station(CSPS) system for a class of varieties of parts arrival to improve the efficiency of operations. When the number of varieties of the system increases, the system state scale will show exponential growth. Considering the dimension disaster problem of traditional $ Q $-learning in the face of the large-scale discrete state and the difficulty of dealing with the look-ahead as a continuous variable directly, the RBF network is introduced to approximate the $ Q $ value function, the input of the RBF network is the state action pair, and the output is the $ Q $ value of the state action pair. The RBF-$ Q $ learning algorithm is proposed, and applied to the optimal control of multi-type products conveyor-serviced production station, realized the continuous action space $ Q $-learning. The simulation analysis is carried out for different varieties, and results show that the method can effectively optimize the processing of CSPS system and improve the learning speed.