Background: Reed has high lignin content, wide distribution and low cost. It is an ideal raw material for replacing wood in the paper industry. Reeds are rich in resources, but the density of reeds is low, leading to high transportation and storage costs. This paper aims to study the compression process of reeds and the creep behaviour of compressed reeds, and provide theoretical guidance for the reed compressor management, bundling equipment and the stability of compressed reed bales.Results: We studied the strain-time relationship and strain of the reed bales compressed under constant force and the creep behaviour of the reed bales under different holding forces. Additionally, the creep behaviour of reed bales under various retention forces was investigated. The test curves are fitted by Machine Learning Prediction Algorithms and Support Vector Machine Regression. And use machine learning prediction algorithm model to establish a quaternary model of reed creep. The results show that the creep behaviour of a reed bale was positively correlated with the initial maximum compressive stress. The established Burgers four-element model was capable of simulating the creep process of reed bales. The test curves coincided well with the model-simulated curves. Reed bales were found to exhibit viscoelasticity. During the creep process, the elastic dynamic force and the viscous resistance were mutually constrained. The strain of reeds was composed of elastic, viscoelastic and plastic εs. And elaborated the three stages of the creep process in detail.Conclusions: We studied the relationship between the strain and time of the reed and the strain and creep behaviour of the reed bag under different holding forces under constant force. It is proved that the multi-layer perceptron network is better than the support vector machine regression in predicting the characteristics of reed materials. The three stages of elasticity, viscoelasticity and plasticity in the process of reed creep are analysed in detail. This article opens up a new way for using machine learning methods to predict the mechanical properties of materials. The proposed prediction model provides new ideas for the characterization of material characteristics.