## 1 Introduction
In the historical data of many industrial processes, due to the inconsistency of sampling frequency, the high cost of quality variable analysis, and the restriction of field environment, the number of process variables that are easy to collect is generally more than that of quality variables that are difficult to collect and analyze. If only the labeled samples are used, and the valuable information contained in the unlabeled samples is ignored, the performance of the soft sensor model will also be restricted due to the limited prior information. Semi-supervised soft sensor modeling evolves from generating pseudo-labels to using unsupervised pre-training and supervised fine-tuning. To use a large number of unlabeled samples to improve the performance of the model, [PERSON] et al. proposed a semi-supervised ensemble SVR soft sensor model by using the idea of generating pseudo-labels in ensemble learning and used an extended pseudo-labeled dataset to improve the performance of the soft sensor model [25]. Considering the dynamic characteristics of industrial processes and the mismatch between quality samples and process samples caused by irregular sampling, [PERSON] et al. proposed a historical feature fusion attention semi-supervised LSTM (HFFA-SSLTM) soft sensor model to extract the historical dynamic information of samples, which significantly improved the prediction accuracy [26]. To obtain a better soft sensor model, it is important to consider the process dynamic characteristics of the samples and the generalization ability of the model. Combined with the mismatch problem between the process samples and the quality samples, these existing problems and the shortcomings of the existing methods motivate this research. Considering the existing problems of industrial processes, this paper proposes a semi-supervised soft sensor modeling method based on SCINet dynamic feature extraction. First, the sample convolution and interaction network with an encoder-decoder structure extracted effective dynamic features unsupervised. Then, the dynamic features were transferred to the XGBoost model [27], which had a strong generalization ability to establish a soft sensor model. The feature extraction ability of the deep network and the strong model's generalization ability were considered. Finally, the effectiveness of the proposed method was verified by the example analysis of the debutane column dataset [28] and the sulfur recovery dataset [29]. The main contributions of this paper are as follows:  
(1) A dynamic feature extractor based on SCINet and autoencoder was designed to extract the dynamic features of all samples in an unsupervised manner, which makes full use of the process information contained in unlabeled samples. (2) The dynamic features of labeled samples were transferred to the XGBoost model to train a regressor, which fully combines the feature extraction ability of deep neural network and the generalization ability of ensemble model. The rest of the paper is organized as follows: The network structure of SCINet is introduced in Section 2. Section 3 describes the XGBoost model. Section 4 presents a dynamic feature based XGBoost model and explains how dynamic features are transferred to XGBoost and the modeling process. In Section 5, full experimental validation is carried out on industrial datasets for performance evaluation. Finally, the conclusion is given in Section 6.