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The quality of parent–child interaction is critical for child cognitive development. The Dyadic Parent–Child Interaction Coding System (DPICS) is commonly used to assess parent and child behaviors. However, manual annotation of DPICS codes by parent–child interaction therapists is a time-consuming task. To assist therapists in the coding task, researchers have begun to explore the use of artificial intelligence in natural language processing to classify DPICS codes automatically. In this study, we utilized datasets from the DPICS book manual, five families, and an open-source PCIT dataset. To train DPICS code classifiers, we employed the pre-trained fine-tuned model RoBERTa as our learning algorithm. Our study shows that fine-tuning the pre-trained RoBERTa model achieves the highest results compared to other methods in sentence-based DPICS code classification assignments. For the DPICS manual dataset, the overall accuracy was 72.3% (72.2% macro-precision, 70.5% macro-recall, and 69.6% macro-F-score). Meanwhile, for the PCIT dataset, the overall accuracy was 79.8% (80.4% macro-precision, 79.7% macro-recall, and 79.8% macro-F-score), surpassing the previous highest results of 78.3% accuracy (79% precision, 77% recall) averaged over the eight DPICS classes. These results show that fine-tuning the pre-trained RoBERTa model could provide valuable assistance to experts in the labeling process.