## 1 Introduction  
In modern industrial manufacturing, continuous casting has been widely used in steel production. However, the continuous casting process is complex and involves many parameters and operation steps. Various cast product defects, such as cracks, segregation and pores, may occur, affecting quality of cast products and potentially causing production stagnation or equipment damage. Therefore, accurately identifying and analyzing the root causes of cast product defects are crucial. Traditional cast product defect analysis mainly relies on experienced engineers who judge the causes of defects by observing the appearance of defects and combining their own experience [1]. This method is effective to a certain extent, but due to the complexity of the continuous casting process and the diversity of influencing factors, it is difficult to grasp all the potential defect causes comprehensively and systematically by manual experience alone. With the development of artificial intelligence and data analysis technology, how to use data-driven methods to analyze the root causes has become a research hotspot. A knowledge graph (KG) is a model that represents knowledge in the form of a graph structure. It connects entities and relations in knowledge through nodes and edges, forming a networked knowledge system. A KG can not only express static relations among entities but also semantically reason about entities and their relations, so as to reveal hidden knowledge and information. With the development of technology, KGs have been widely used in various domains to form the so-called Domain-Specific KGs (DSKGs). DSKGs can significantly improve the effect of information retrieval, knowledge discovery, decision support and other tasks in a specific domain [2]. For example, in the medical domain, DSKGs can help doctors diagnose diseases [3] and perform medical research and clinical practice [4] more accurately by integrating patient data. In the manufacturing domain, DSKGs can optimize production processes, predict equipment failures and improve product quality [5]. In the financial domain, DSKGs can assist in risk assessment and intelligent investment advisory [6]. In the root cause analysis of cast product defects, the Continuous Casting Quality Knowledge Graph (C2Q-KG) can integrate different process parameters, equipment status and environmental factors in the continuous casting process. Through the systematic analysis of the correlation of this information, the causes of cast product defects can be reasoned out more objectively. However, existing knowledge graph reasoning methods often use the same reasoning method when dealing with different types of relation features and fail to fully consider the difference in the number distribution of head and tail entities in the relation. Applying the same treatment leads to uneven reasoning accuracy, and the reasoning performance is often unsatisfactory. In order to solve this problem, this paper proposes a knowledge graph reasoning model based on a two-branch reasoning network (KGTBRN), which aims to improve the accuracy of root cause analysis for cast product defects and provide powerful decision support for quality control and process optimization in production for continuous casting enterprises. Our contributions in this paper are as follows:  
* We propose KGTBRN in the continuous casting domain. Aiming at the requirements of root cause analysis for cast product defects, one connection layer and one capsule layer are used to deal with different types of entity-side reasoning tasks, so as to improve both the adaptability of KGTBRN to different relation features and the reasoning accuracy. * KGTBRN is experimentally verified on a continuous casting data set and compared with existing cutting-edge methods. The experimental results show that KGTBRN obtains the best mean reciprocal rank and the highest HITS@1, HITS@3 and HITS@10 on the continuous casting data set.