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Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older. The aim of this study is to predict sequential treatment plans from electronic dental records. We construct a clinical decision support model, MultiTP, explores the unique topology of teeth information and the variation of complicated treatments, integrates deep learning models (convolutional neural network and recurrent neural network) adaptively, and embeds the attention mechanism to produce optimal treatment plans. MultiTP shows its promising performance with an AUC of 0.9079 and an F score of 0.8472 over five treatment plans. The interpretability analysis also indicates its capability in mining clinical knowledge from the textual data. MultiTP's novel problem formulation, neural network framework, and interpretability analysis techniques allow for broad applications of deep learning in dental healthcare, providing valuable support for predicting dental treatment plans in the clinic and benefiting dental patients. The MultiTP is an efficient tool that can be implemented in clinical practice and integrated into the existing EDR system. By predicting treatment plans for partial edentulism, the model will help dentists improve their clinical decisions. • Predict sequential dental treatment plans from textual data through a newly designed deep learning model which integrates CNN and RNN. • Demonstrate that our workflow achieves the best overall performance compared to a variety of machine learning approaches. • Incorporate spatial and temporal characteristics of dental data into the proposed deep learning model to improve the performance which was validated by the clinical data. • Demonstrate the deep learning model's interpretability by pinpointing key factors aligned with established medical knowledge. [ABSTRACT FROM AUTHOR]