Knowledge tracing models mainly use three types of learning behaviors data， including learning process， learning end and learning interval， but the existing studies do not fuse the above types of learning behaviors and cannot accurately describe the interactions of multiple types of learning behaviors. To address these issues， a Multi-Learning Behavior collaborated Knowledge Tracing （MLB-KT） model was proposed. First， the multi-head attention mechanism was used to describe the homo-type constraint for each type of learning behavior， then the channel attention mechanism was used to model the multi-type collaboration in three types of learning behaviors. Comparison experiments of MLB-KT， Deep Knowledge Tracing （DKT） and Temporal Convolutional Knowledge Tracing with Attention mechanism （ATCKT） models were conducted on three datasets. Experimental results show that the MLB-KT model has a significant increase in Area Under the Curve （AUC） and performs best on ASSISTments2017 dataset， the AUC is improved by 12.26% and 2.77% compared to DKT and ATCKT respectively； the results of the representation quality comparison experiments also verify that the MLB-KT model has better performance. In summary， modeling the homo-type constraint and multi-type collaboration can better determine students' knowledge status and predict their future answers.