The chemical industry, which is also a process industry, mainly uses energy, equipment and other resources to mix or separate various components and cause chemical reactions to achieve value-added purposes. Due to process or control and other reasons, there will be some abnormalities in the chemical production process, resulting in unstable product quality. Therefore, abnormal identification and diagnosis of chemical production are needed to stabilize product quality. In actual chemical reactions, it is difficult to directly model due to problems such as reaction mechanism and parameters. However, the chemical production process stores a large amount of parameter data that changes with time. These data reflect the production status information and product result information to a certain extent. Therefore, these time series parameter data can be used to mine production process information and conduct abnormal research. Based on the research on the production line of energetic explosive in a workshop in Gansu Province, this paper analyzes the chemical production process of energetic explosive RDX and studies the factors that affect the production. Based on the historical production data of RDX in the workshop, this paper distinguishes and diagnoses the abnormal production process data in the chemical process. The main work of this article is as follows: 1) Analyze and study the production background and production process characteristics of the energetic material RDX in a workshop in Gansu. According to the characteristics of RDX's actual chemical production line, a research idea of abnormal chemical production process based on time series data mining is proposed. At the same time, according to the problems existing in the historical process parameter data collected by RDX's production line, it performs missing value filling processing and data conversion. 2) After data preprocessing, in order to be able to extract the trend characteristics of the time series, realize the piecewise linear representation of the process time series parameters in chemical production. For the current time series linear representation is mostly limited the evaluation of adjacent extreme points or feature points with large fluctuations is used to screen and determine the selection of segment points, which is easy to fall into the local optimum and cannot accurately represent the time series trend. This paper proposes a problem based on turning points and Time series trend feature extraction algorithm for trend segments. Through two sets of experiments, this method not only has good anti-noise ability and trend extraction ability, but also has better fitting accuracy under the same compression rate, which can be used for subsequent data mining. Provide better features. 3) In this paper, the chemical production process itself has an unstable overall operation. The abnormal detection of nuclear principal component analysis is often based on a single time sampling. This detection method ignores the time characteristics of the parameters and is not applicable to the chemical production conditions of this subject. Therefore, this paper A detection method based on the time series trend characteristics is proposed. Based on the PLA of time series, the characteristics of the reaction trend of the operating conditions are constructed to make up for the shortcomings of the nuclear principal component analysis and detection method, and the process of abnormal production can be identified, and Realize the diagnosis of abnormal causes.