As large-scale systems continue to grow in complexity, effectively leveraging the multi-dimensional information contained in semi-structured logs has become a critical challenge for ensuring system stability. Existing methods rely on limited log content, which restricts semantic modeling capability and leads to degraded performance when abnormal samples are scarce or data distributions are imbalanced. To address these challenges, we propose RELog, an efficient and robust anomaly detection framework that comprehensively utilizes multi-dimensional log features. RELog adopts a hierarchical process for efficient log parsing and anomaly detection. During log parsing, Large Language Models are introduced to enhance the semantic understanding of low-confidence templates generated by the heuristic method. For anomaly detection, we design dedicated encoders for templates, parameters, and time features, tailored to the low complexity and high redundancy of logs, enabling discriminative embeddings. Furthermore, we propose an efficient hybrid sequence encoder integrating state-space modeling and attention for capturing continuous log patterns and critical dependencies, complemented by a replacement classification task for imbalanced data. Experimental results demonstrate that RELog achieves high-accuracy log parsing and anomaly detection while maintaining efficiency. Moreover, RELog demonstrates robustness across varying anomaly ratios and dynamic environments, effectively detecting diverse types of anomalies reflected by parameter and time features.