Incremental　learning　aims　to　train　a　model　on　a　sequence　of　tasks　while　preserving　previously　learned　knowledge,　whereas　catastrophic　forgetting　is　a　widely-studied　problem.　To　tackle　this　concern,　we　design　a　multi-level　knowledge　distillation　framework　(MLKD),　which　combines　coarse-grained　and　fine-grained　distillations　to　effectively　memorize　past　knowledge.　For　the　coarse-grained　distillation,　we　enforce　the　model　to　memorize　the　neighborhood　relationships　among　samples.　For　the　fine-grained　distillation,　we　aim　to　memorize　the　activation　logits　within　each　sample.　Through　the　multi-level　knowledge　distillation,　we　can　learn　more　robust　incremental　learning　models.　In　order　to　assess　the　efficacy　of　the　MLKD,　we　perform　experiments　on　two　popular　incremental　learning　benchmarks(CIFAR100　and　Mini-ImageNet),　and　our　approach　achieves　good　performance.