As an emerging field of machine learning, multi-source partial domain adaptation (MSPDA) poses challenges to related research due to the complexities of the involved source domains, the diversities between the domains, and the unsupervised nature of the target domain itself, leading to rarely few works presented. In this scenario, the irrelevant class samples in multiple-source domains will cause large cumulative errors and negative transfer during domain adaptation. In addition, most of the existing multi­source domain adaptation methods do not consider the different contributions of different source domains to the target domain tasks. Therefore, this study proposes an adaptive weight­induced multi­source partial domain adaptation (AW-MSPDA). Firstly, a diverse feature extractor is constructed to effectively utilize the prior knowledge of the source domain. Meanwhile, multi­level distribution alignment strategy is constructed to eliminate distribution discrepancies from different levels to promote positive transfer. Moreover, the pseudo­label weighting and similarity measurement are used to construct adaptive weights to quantify the contribution of different source domains and filter samples which are irrelevant to the source domain. Finally, the generalization and performance superiority of the proposed AW­MSPDA algorithm are evaluated by extensive experiments.