Deep learning based optimization of personalized psychological counseling strategies

Personalized psychological counseling is essential for tailoring therapeutic strategies to individual needs. However, existing methods often rely on static approaches that fail to dynamically adapt responses based on emotional and contextual changes in client conversations. The primary objective is to improve personalized psychological counseling for college students experiencing academic stress, social adjustment challenges, and emotional well-being concerns. The proposed model is termed the Grizzly Bear Fat-Increase Optimization–Driven Encoder–Decoder Long Short-Term Memory (GBFI–EDLSTM) architecture to dynamically adjust counseling strategies. The model utilizes an Encoder–Decoder LSTM to process and generate personalized counseling responses. GBFI fine-tunes these responses by analyzing emotional cues, ensuring that the generated strategies are tailored to the client’s evolving needs within a college academic and social environment. The model is trained on a curated dataset consisting of mental health conversation dialogues collected from college students and university counseling sessions, which contain text-based interactions between student clients and professional counselors. Preprocessing steps such as tokenization, stop-word removal, lemmatization, and punctuation removal to standardize the input. BERT embeddings are used for feature extraction, enabling the model to capture meaningful semantic relationships. These features assist the model in generating contextually appropriate and emotionally relevant responses. The model is implemented using Python, with Tensorflow and Keras for DL, and NLTK and spaCy for text preprocessing. The model accuracy (0.936), precision (0.93), recall (0.92), and F1-score (0.92), along with a PHQ-9 reduction (4.3) and dropout rate (8.8%). It outperforms traditional models in terms of emotional engagement and therapeutic progress prediction.