Piano improvisation accompaniment ability, as an important part of piano education, lacks scientific and effective teaching means and cannot be well adapted to the intelligent era. In this paper, we focus on melodic adaptation and stylistic evolution to build a dynamic interactive intelligent piano accompaniment generation framework. The framework is based on the extraction of piano music features, based on the Mido library to realize the preliminary analysis of MIDI files, combined with the event information of different tracks to obtain the note feature matrix. Then, oriented to classical reinforcement learning RLTUNER baseline model, LSTM gates are used as inputs for optimization to generate piano accompaniment melodies. On the other hand, in this paper, rhythmic features are used as the stylistic representation of piano music, and Actor and Critic are used as the generative and discriminative networks to construct the rhythm generation mechanism, respectively. It is found that the generative framework of this paper has high accuracy and strong generalization ability in multi-track processing, with an accuracy rate of 89.78% and an error rate of only 0.047% in the test set. Meanwhile, the accompaniment music generation results effectively balance originality and recognizability, and are recognized by the audience.
