One　of　the　major　challenges　of　transfer　learning　algorithms　is　the　domain　drifting　problem　where　the　knowledge　of　source　scene　is　inappropriate　for　the　task　of　target　scene.　To　solve　this　problem,　a　transfer　learning　algorithm　with　knowledge　division　level　(KDTL)　is　proposed　to　subdivide　knowledge　of　source　scene　and　leverage　them　with　different　drifting　degrees.　The　main　properties　of　KDTL　are　three　folds.　First,　a　comparative　evaluation　mechanism　is　developed　to　detect　and　subdivide　the　knowledge　into　three　kinds--the　ineffective　knowledge,　the　usable　knowledge,　and　the　efficient　knowledge.　Then,　the　ineffective　and　usable　knowledge　can　be　found　to　avoid　the　negative　transfer　problem.　Second,　an　integrated　framework　is　designed　to　prune　the　ineffective　knowledge　in　the　elastic　layer,　reconstruct　the　usable　knowledge　in　the　refined　layer,　and　learn　the　efficient　knowledge　in　the　leveraged　layer.　Then,　the　efficient　knowledge　can　be　acquired　to　improve　the　learning　performance.　Third,　the　theoretical　analysis　of　the　proposed　KDTL　is　analyzed　in　different　phases.　Then,　the　convergence　property,　error　bound,　and　computational　complexity　of　KDTL　are　provided　for　the　successful　applications.　Finally,　the　proposed　KDTL　is　tested　by　several　benchmark　problems　and　some　real　problems.　The　experimental　results　demonstrate　that　this　proposed　KDTL　can　achieve　significant　improvement　over　some　state-of-the-art　algorithms.