Online platforms have enabled users to create and share multimodal content, fostering new forms of personal expression and cultural interaction. Among these, memes—combinations of images and text—have become a prevalent mode of digital communication, often used for humor, satire, or social commentary. However, memes can also serve as vehicles for spreading misogynistic messages, reinforcing harmful gender stereotypes, and targeting individuals based on gender. In this work, we investigate the effectiveness of various multimodal models for detecting misogynistic content in memes. We propose a BERT+CLIP+LR model that integrates BERT’s deep contextual language understanding with CLIP’s powerful visual encoder, followed by Logistic Regression for classification. This approach leverages complementary strengths of vision-language models for robust cross-modal representation. We compare our proposed model with several baselines, including the original CLIP+LR, and traditional early fusion methods such as BERT + ResNet50 and CNN + InceptionV3. Our focus is on accurately identifying misogynistic content in Chinese memes, with careful attention to the interplay between visual elements and textual cues. Experimental results show that the BERT+CLIP+LR model achieves a macro F1 score of 0.87, highlighting the effectiveness of vision-language models in addressing harmful content on social media platforms.