Lightweight Actor-Critic generative adversarial network-based medical question and answer generation system

The invention discloses a lightweight Actor-Critic generative adversarial network-based medical question and answer generation system, which comprises a generator and a discriminator of a lightweight Actor-Critic structure, and is characterized in that after the system inputs a medical question proposed by a user, the generator generates a medical diagnosis scheme in a coding-decoding mode; a known medical question and answer text serves as a data set and is input into a generator, a maximum likelihood estimation method is adopted for pre-training, data distribution generated by the pre-trained generator serves as a false sample, known data serves as a true sample, and the data distribution and the known data are input into a discriminator network for pre-training. The invention also includes pre-training a generator and a discriminator, multiplexing the generator as an Actor network and constructing a Critic network of which the structure is a long-short-term memory network, updating the network weight param