Concerning the problem that the existing Heterogeneous Graph Neural Networks （HGNNs） rely on manually constructed meta-paths and fail to fully capture complex relation semantics， a Multi-Head Relation-Transformed Heterogeneous Graph neural Network （MHRTHGN） method was proposed. In this method， independent relation transformation matrices were constructed for each type of relation， a multi-head attention mechanism was introduced to characterize multi-relation features precisely， and a relation-specific multi-head attention mechanism was designed to fuse multi-relation information at both node-level and relation-level. At the same time， a Feed-Forward Network （FFN） was combined to further enhance semantic expressiveness capability of the model. Experimental results show that on the ACM， IMDB， and DBLP datasets， the proposed method improves the accuracies of node classification and clustering tasks by 3-5 percentage points on average compared with representative baseline models such as HAN （Heterogeneous graph Attention Network） and Graph Convolutional Network （GCN）， validating its effectiveness and performance superiority in multi-relation heterogeneous graph learning. It can be seen that MHRTHGN method can model multi-relation information without relying on meta-path prior， providing a more generalizable solution with higher application potential for heterogeneous graph representation learning.