The frequency distribution of International Classification of Diseases （ICD） codes is long tail， resulting in it is challenging to perform multi-label text classification for few-shot code. An MNIC （Meta Network-based automatic ICD Coding model） was proposed to solve the problem of insufficient training data in few-shot code classification. Firstly， instances in the feature space and features in the semantic space were fitted to the same space for mapping， and the feature representations of many-shot codes were mapped to their classifier weights， thus learning meta-knowledge through meta-network. Secondly， the learned meta-knowledge was transferred from data-abundant many-shot codes to data-poor few-shot codes. Finally， a reasonable explanation was provided for the transferability and generality of meta-knowledge. Experimental results on MIMIC-Ⅲ dataset show that MNIC improves the Micro-F1 and Micro Area Under Curve （Micro-AUC） of few-shot codes by 3.77 and 3.82 percentage points respectively compared to the suboptimal AGM-HT （Adversarial Generative Model conditioned on code descriptions with Hierarchical Tree structure） model， indicating that the proposed model improves the performance of few-shot code classification significantly.