Unsupervised representation learning on hypergraphs has recently drawn increasing attention due to its ability to capture high-order relationships without requiring labeled data. However, existing hypergraph contrastive learning methods predominantly follow spatial-based paradigms that rely on message-passing frameworks, which largely emphasize low-pass filtering. This restricts their ability to adapt to the diverse spectral characteristics of real-world hypergraphs. Motivated by the observation that different hypergraph datasets exhibit varied frequency energy distributions, we propose HyperAim, a novel contrastive learning framework that incorporates adaptive multi-frequency filtering into hypergraph representation learning. HyperAim integrates three complementary channels: a low-pass spatial channel, a high-pass spatial channel, and a spectral channel based on framelet transforms that jointly capture multi-frequency components. To fully exploit these diverse views, we introduce a frequency-aware contrastive learning strategy that constructs perturbed views via spectral and structural augmentations and enforces consistency across representations through inter- and intra-channel objectives. Extensive experiments on multiple benchmark datasets demonstrate that HyperAim consistently outperforms state-of-the-art baselines. Ablation studies further verify the effectiveness of adaptive frequency decomposition and frequency-aware contrastive learning in enhancing hypergraph representations.