###### Abstract  
As an important condition for fatigue analysis and life prediction, load spectrum is widely used in various engineering fields. The extrapolation of load samples is an important step in compiling load spectrum. It is of great significance to select an appropriate load extrapolation method. This paper proposes a load extrapolation method based on long short-term memory (LSTM) network, introduces the basic principle of the extrapolation method, and applies the method to the data set collected under the working state of 5 MN metal extruder. The comparison between the extrapolated load data and the actual load shows that the trend of the extrapolated load data is basically consistent with the original tendency. In addition, this method is compared with the rain flow extrapolation method based on statistical distribution. Through the comparison of the short-term load spectrum compiled by the two extrapolation methods, it is found that the load spectrum extrapolation method based on LSTM network can better realize load prediction and optimize the compilation of load spectrum. information is difficult to obtain directly. Load extrapolation has become the key point in the compilation of load spectrum. In order to solve different problems in various fields of engineering, a variety of extrapolation methods have been developed, such as rain flow matrix extrapolation [6], peak threshold extrapolation [7], mileage and quantity extrapolation [8], and parameter extrapolation [9; 10]. The earliest applied load spectrum extrapolation technique is the rain flow extrapolation method. This method is based on mathematical statistics and completes the extrapolation of short-term load spectrum based on statistical characteristics. Initially, the extrapolation of the load spectrum was a cyclic counting method of short-term load-time history, and the load amplitude was extrapolated after fitting the amplitude distribution according to the amplitude-frequency histogram. However, this method only considers the influence of load amplitude on fatigue damage and ignores the mean load, and the final load spectrum is not perfect. Subsequently, scholars such as [PERSON] performed rain flow counting processing on random loads, used mixed two-parameter Weibull distribution to fit the load amplitude and applied it to forklift parts to achieve parameter extrapolation [11; 12]. On the basis of one-dimensional amplitude extrapolation, [PERSON] extended the idea of two-dimensional rain flow matrix, using mixed Weibull distribution and mixed normal distribution to fit the amplitude and mean of the load respectively. According to the joint probability density function of the load, a parameter rain flow extrapolation method based on mixed distribution is proposed [13]. Due to the wide application range of statistical distribution and good extrapolation effect, the extrapolation of rain flow load spectrum based on mathematical statistics has gradually attracted people's attention and has been widely used. Although the mixed distribution has a good fitting effect on the load, the parameter estimation process is relatively cumbersome, and the calculation time becomes the main problem of this method. For the weak periodicity of the load signal generated during the service of the extruder, a simple linear model is difficult to obtain good prediction accuracy. With the development of artificial intelligence, deep learning methods have flourished and are gradually applied to solve corresponding problems in the engineering field. Under the guidance of deep learning theory, many variant models of neural networks are proposed, which can learn complex nonlinear data well. At present, as one of the important branches of deep learning, recurrent neural network (RNN) has achieved many successes in the fields of computer vision [14], speech recognition [15] and natural language processing [16]. Because of the memory ability of RNN model to time series data, it is widely used in data prediction.