Smart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency. The RPNN model is trained for toothbrush posture recognition and brushing position and then monitors the correctness and integrity of the Bass Brushing Technique. Compared to conventional deep learning models, the recognition accuracy of RPNN is 99.08% in our experiments, which is 16.2% higher than that of the Convolutional Neural Network (CNN) and 21.21% higher than the Long Short-Term Memory (LSTM) model. The model we used can greatly reduce the computing power of hardware devices, and thus, our system can be used directly on smartphones.