A multimodal sequential recommendation algorithm based on contrastive learning technology was proposed to improve the accuracy of sequential recommendation algorithm by using multimodal information of commodities. Firstly， to obtain the visual representations such as the color and shape of the product， the visual modal information of the product was extracted by utilizing the contrastive learning framework， where the data enhancement was performed by changing the color and intercepting the center area of the product. Secondly， the textual information of each commodity was embedded into a low-dimensional space， so that the complete multimodal representation of each commodity could be obtained. Finally， a Recurrent Neural Network （RNN） was used for modeling the sequential interactions of multimodal information according to the time sequence of the product， then the preference representation of user was obtained and used for commodity recommendation. The proposed algorithm was tested on two public datasets and compared with the existing sequential recommendation algorithm LESSR. Experimental results prove that the ranking performance of the proposed algorithm is improved， and the recommendation performance remains basically unchanged after the feature dimension value reaches 50.