Log anomaly detection is a critical aspect of Artificial Intelligence for IT Operations (AIOps), as it enables the timely identification of system failures, thereby facilitating program understanding throughout the entire software maintenance and engineering life cycles. While existing methods only leverage raw log information for anomaly detection, they struggle to address challenges such as log differences due to log evolution, noise from log parsing, and stylistic differences between logs and natural language. To overcome these limitations, we propose GELog, an innovative log anomaly detection method. Specifically, GELog initially employs GPT to semantically augment log templates. Subsequently, it extracts semantic vectors using pre-trained sentence-bert and introduces an attention-based semantic fusion module that integrates the semantic representations of both the original and augmented logs. Finally, GELog utilizes a Transformer-based model for anomaly detection. We evaluated the performance of GELog on four publicly available datasets, and the experimental results demonstrate that GELog significantly enhances the semantic representation of logs, achieving superior anomaly detection performance.