To investigate the texture features of recognition memory EEG and address the issue of structural instability in extracting EEG texture features using vertical symmetric local graph structure (VSLGS) and symmetric local graph structure (SLGS). A recognition memory experiment was designed based on new and old paradigms, and relevant EEG data were collected from 35 medical students and 35 non-medical students. The EEG data were categorized into six different stages: learning medical images, learning non-medical images, recognizing old medical images, recognizing old non-medical images, recognizing new medical images, and recognizing new non-medical images.Firstly, a 2D wavelet transform was applied to obtain three subbands for each participant′s EEG data. Then, an improved integrated local graph structure method was proposed to extract features from the original data and the three subbands. This improved algorithm incorporated extended symmetric local graph structures (ESLGS) and composite local graph structures (CLGS) with stable structures. The features were then normalized to avoid overfitting, and feature matrix columns with correlation coefficients between 0.8 and 1 were selected using Pearson correlation coefficients.The improved algorithm was validated on classifiers such as support vector machines (SVM), and the model was evaluated using four metrics: accuracy, precision, recall, and F1 score. Compared to the original algorithm, the improved algorithm achieved an increase of 3.8%, 0.4%, 0.3%, 1.6%, 5.1%, and 4.2% in classification accuracy on support vector machines for each condition.The classification results indicate significant differences between medical students and non-medical students in the recognition stage of learning medical images. The addition of ESLGS and CLGS demonstrates better classification performance compared to the original algorithm, which utilized VSLGS and SLGS.