This study participates in the ROCLING-2025 shared task on Chinese dimensional sentiment analysis for medical self-reflection texts. Dimensional Sentiment Analysis (DSA) represents emotions as continuous dimensions, such as valence (positive to negative) and arousal (calm to excited), providing finer-grained representations compared to traditional categorical approaches, which are suitable for applications in mental health monitoring and risk assessment. We use large language models (LLMs) to extract contextual embedding vectors, which are then fed into regression models, such as Support Vector Regression (SVR), for valence-arousal prediction. The training data consists of the Chinese EmoBank dataset (2,954 general-domain samples), the validation data is a Medical Self-Reflection Corpus Dataset (994 samples), and the test data is another Medical Self-Reflection Corpus Dataset (1,541 samples). Experimental results show that the SVR model with DeepSeek embeddings performs best. Multi-model ensemble learning further improves performance to 0.463 valence MAE, 0.759 arousal MAE, 0.805 valence PCC, and 0.608 arousal PCC. This approach shows the potential of multi-model fusion in DSA for biomedical applications, facilitating the development of non-intrusive mental health assessment tools.