With the continuous development of intelligent technology, robots have entered various industries. Firefighting robots have become a hot topic in the field of firefighting and rescue equipment. For firefighting robots, autonomous firefighting technology is the core capability. It includes three steps: flame recognition, fire source location, autonomous firefighting. At present, flame recognition has been widely studied, but the adaptability is poor for different flames. For fire source location technology only rough positioning has been achieved. For autonomous firefighting, the time-consuming water point feedback adjustment method is often used. Those are not suitable for the actual fire rescue. So we proposed to use the visual information, thermal imaging morphological and thermal data of the flame for deep learning, which greatly improves the adaptability of flame recognition. The centimeter-level high-precision positioning of the fire source is achieved. Finally, the proposed water cannon fire source projection method is used to realize rapid water cannon movement instruction generation, and achieve rapid autonomous firefighting. The test results show that the proposed fire source identification algorithm can identify all fire sources up to 15 m at a speed about 15Fps. It can rapid autonomous firefighting within 0.5 s.