The scarcity of well-structured language resources defeats some natural language processing algorithms to achieve higher performance in low-resource languages. This paper addresses the problem of morphological transfer learning between two languages, proposes an evaluation metric of morphological transfer ratio to estimate the transfer effect, and carries out the transfer experiments of multiword expression (MWE) extraction and neural machine translation (NMT) between any two languages in the three Austronesian languages of Indonesian, Malay and Filipino. In the experiment of MWE extraction, we use a corpus of Language1 to extract MWEs of Language2. In the NMT experiment, we train a Language2-Chinese NMT model on the Language1-resource-enhanced Language2-Chinese parallel sentence corpus. The experimental results show that morphological transfer learning is effective in the tasks of language resource construction and semantic paraphrasing application for low-resource languages, and prove that, due to the linguistic homology and morphological similarity within the same language family, the cross-language morphological reuse of the same language family has stronger transferability and computability.