In NAND flash-based solid-state drives (SSDs), system performance and quality of service tend to be degraded by frequent conflicts between garbage collection (GC) I/Os and user I/Os. To address this challenge, we propose an adaptive workload burst-Aware Garbage Collection (AGC) mechanism to build highperformance SSDs. AGC minimizes conflicts between user I/Os and GC I/Os by jointly considering access patterns in workloads, internal GC, and allocation strategies. The running time of a workload is divided into multiple five-millisecond time windows. AGC incorporates a burst I/O access pattern prediction mechanism, which anticipates I/Os patterns in subsequent time windows according to historical access patterns. Pattern predictions allow AGC to proactively buffer valid pages during GC to prevent interference with forthcoming user I/O requests. According to the status of the underlying flash memory, we implement a hybrid allocation strategy to reduce GC-induced user writes blocking while preserving read parallelism performance. AGC classifies data into four hotness levels (hot, warm, cool, and cold) according to the update interval of the same request. Then, we design a hotness-aware victim block selection policy that prioritizes hot blocks to avert valid data migration during GC, thereby reducing write amplification in SSDs. We evaluate AGC through extensive experiments driven by real-world traces. The findings confirm that compared with state-of-the-art schemes (Baseline, CachedGC, FFT-GC, HIPA, and RDA), AGC reduces read and write response time by up to 81.57% and 69.66%, respectively, with average reductions of 54.34% and 39.21%. Furthermore, AGC decreases GC counts by up to 24.31% with an average of 12.04%, thereby reducing GC overhead and extending SSD lifetime across diverse workloads.