Significant progress has been made in sequential recommendation systems for capturing the evolution of user interests， where the effective use of side information plays a crucial role. For example， item-related text data is fused with item ID in sequential recommendation effectively in the transformed domain space， and this text-ID semantic fusion is then used for sequential recommendation， so as to enhance recommendation performance. The existing models suffer from limitations such as the complex user behaviors， limited generalization capability after heterogeneous information fusion， and data sparsity. Therefore， a text-ID sequential recommendation model based on multi-strategy contrastive learning and adaptive label smoothing named MCLALS （Multi-strategy Contrastive Learning and Adaptive Label Smoothing） was proposed. First， textual representations were processed through a domain-expert fusion network and knowledge from different feature subspaces were integrated dynamically. Second， the processed text and ID representations were fused semantically at sequence level through frequency-domain transformation to capture deep interactions among heterogeneous information. Third， on the fused high-level representation， multi-strategy contrastive learning was applied to generate diverse augmented representations using multiple complementary data augmentation strategies， thereby alleviating data sparsity effectively. Finally， adaptive label smoothing was adopted to optimize the training objective， thereby enhancing the model’s robustness to noise and long-tail distribution. MCLALS was evaluated systematically on three public benchmark datasets： Food， Office， and OR. Experimental results show that the proposed model outperforms several baseline models， including SASRec （Self-Attentive Sequential Recommendation） and TedRec （Text-ID Semantic Fusion for Sequential Recommendation）， in overall performance. In particular， on the OR dataset， MCLALS achieves at least 6.69% improvement in NDCG@10 compared to the best baseline model， verifying its effectiveness.