Multi-modal representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods in recent years. In this work, in order to better excavate high-level semantic information, we leverage LLMs’ profound linguistic knowledge to provide semantic compensation for skeleton embedding. Specifically, we present a Contrastive Language-Skeleton Pre-training framework (SkeletonCLSP), which employs cross-modal multivariate features integration and anomaly distribution auxiliary correction for self-supervised action representation. In the preprocessing phase, the Distributed Difference Perception (DDP) module is first proposed to emendate the anomaly fusion features by simulating the distribution of skeleton features in the fusion sample. Subsequently, utilizing the distribution to estimate anomalies in the inference stage. Furthermore, to implement parallel sequence modeling of text and skeleton embedding, we incorporate a pre-fusion encoder called Text-Skeleton Vision Encode (TSVE), encouraging a richer exchange of information between the multi-modal data. Finally, Text Context Broadcasting (TCB) is proposed to infuse dense interactions instead of sparse interactions into the skeleton spatial-temporal representation by giving each sample uniform attention. Extensive experiments on NTU60, NTU120, PKU-MMD, and UAV-Human show that the proposed method achieves remarkable action recognition performance. Related code will be available on .