Long-term incorrect sitting undoubtedly will damage physical health. Recognizing bad sitting posture has been of particular interest recently due to the prevailing Internet of Healthcare Things (IoHT). While various sitting posture recognition systems based on wearable devices and cameras are designed, they expose two obvious weaknesses. First, the sensors attached to the body will cause inconvenience to users, and using a camera requires high energy consumption and faces the risk of user privacy leakage. Second, most of these systems require massive training samples to build models, and the recognition performance of certain models on new user data with significant sample distribution differences remains poor. In this work, we propose SmartSit, the first-ever robust sitting posture recognition system with smartphone acoustic sensing. We start by designing a signal detection algorithm to determine the boundary of the sitting posture signal through a series of signal transformation methods. Then we construct the sitting posture recognition module MG-Reptile by modifying the meta-learning method by combining the Distributed Measurement Strategy (DMS) and Generative Adversarial Network (GAN). We show that the designed system is immune to the low generalization performance with only a few training samples. The observed testing results further validate the effectiveness and robustness of SmartSit.