Autism Spectrum Disorder (ASD) is a multifactorial-driven neurodevelopmental disorder, the pathophysiological mechanisms of which remain largely elusive, significantly hampering the development of effective therapeutic strategies. MicroRNAs (miRNAs) are emerging as critical regulators in the molecular etiology of autism, influencing gene expression by interacting with target mRNAs involved in neural development and synaptic function. To introduce miRNA-gene regulatory information to identify potential autism-related genes and predict drug targets, we introduce a multitask learning approach named ARGENT based on the heterogeneous graph convolutional network (HGCN). The heterogeneous graph in ARGENT includes nodes representing genes, miRNAs, drugs and integrates the regulatory relationships between miRNA-gene, gene-gene, and the associations between genes and drugs. Using the HGCN, the proposed model is able to learn embeddings for these nodes by aggregating information from different adjacent node types, enabling the prediction of autism-related genes and their potential drug candidates. The efficacy of ARGENT is substantiated through multiple validation experiments, demonstrating that it not only identifies known autism-related genes but also reveals novel candidate genes and potential drug targets. Additionally, KEGG and GO analysis results of known genes related to ASD and genes predicted by ARGENT as potentially related to ASD reveal that these genes are significantly enriched in pathways associated with neural signal transmission. This suggests that these genes may play crucial roles in the core functions and structure of neural circuits, potentially influencing the neurodevelopmental aspects of ASD, providing new insights for prevention and drug repositioning.