The integration of computer-science-oriented and neuroscience-oriented approaches is believed to be a promising way for the development of artificial general intelligence (AGI).Recently,a hybrid Tianjic chip that integrates both approaches has been reported,providing a general platform to facilitate the research of AGI.The control algorithm for handling various neural networks is the key to this platform;however,it is still primitive.In this work,we propose a hybrid neural state machine (H-NSM) framework that can efficiently cooperate with artificial neural networks and spiking neural networks and control the workflows to accomplish complex tasks.The H-NSM receives input from different types of networks,makes decisions according to the fusing of various information,and sends control signals to the sub-network or actuator.The H-NSM can be trained to adapt to context-aware tasks or sequential tasks,thereby improving system robustness.The training algorithm works correctly even if only 50％ of the forced state information is provided.It achieved performance comparable to the optimum algorithm on the Tower of Hanoi task and achieved multiple tasks control on a self-driving bicycle.After only 50 training epochs,the transfer accuracy reaches 100％ in the test case.It proves that H-NSM has the potential to advance control logic for hybrid systems,paving the way for designing complex intelligent systems and facilitating the research towards AGI.