To solve the problems such as high data transmission delay and weak dynamic adaptability of underwater acoustic communication, this study proposes an intelligent underwater acoustic network routing protocol based on Q-learning ant colony optimization (QACO). The protocol includes routing behavior and intelligent decision. In the route discovery and maintenance phase, the construction of the network topology environment and information exchange among nodes as well as the network maintenance are carried out by intelligent NetAnts. In the Q-learning phase, the node energy and depth and network transmission delay learning characteristics are quantified as discount factors and learning rates to extend the network lifecycle and reduce system energy consumption and delay. Finally, simulations are carried out through the underwater acoustic network environment, and the experimental results show that QACO outperforms the Q-learning aided ant colony routing protocol (QLACO), Q-learning-based energy-efficient and lifetime-aware routing protocol (QELAR), and depth-based routing (DBR) algorithm in terms of energy consumption, delay, and network lifecycle.