###### Abstract  
A dynamic uncertain causality graph (DUCG) is a probabilistic graphical model for knowledge representation and reasoning, which has been widely used in many areas, such as probabilistic safety assessment, medical diagnosis, and fault diagnosis. However, the convention DUCG model fails to model experts' knowledge precisely because knowledge parameters were crisp numbers or fuzzy numbers. In reality, domain experts tend to use linguistic terms to express their judgements due to professional limitations and information deficiency. To overcome the shortcomings of DUCGs, this article proposes a new type of DUCG model by integrating Pythagorean uncertain linguistic sets (PULSs) and the evaluation based on the distance from average solution (EDAS) method. In particular, experts express knowledge parameters in the form of the PULSs, which can depict the uncertainty and vagueness of expert knowledge. Furthermore, this model gathers the evaluations of experts on knowledge parameters and handles conflicting opinions among them. Moreover, a reasoning algorithm based on the EDAS method is proposed to improve the reliability and intelligence of expert systems. Lastly, an industrial example concerning the root cause analysis of abnormal aluminum electrolysis cell condition is provided to demonstrate the proposed DUCG model. Although the DUCGs have been utilized to solve various issues, the conventional DUCG model has been criticized for its shortcomings in knowledge representation and reasoning (KRR) [15]. For example, the knowledge parameters acquired from historical statistics or experts were restricted to be crisp values [16]. However, due to information scarcity, complex causalities between events, and inconsistent knowledge of specialists, the evaluations obtained from experts are usually uncertain and fuzzy [17; 18; 19]. Hence, to handle the vague knowledge, different uncertainty theories such as intuitionistic fuzzy sets [20], cloud model theory [21], and picture fuzzy sets [22] have been combined with DUCGs to improve their KRR ability. In practice, experts tend to use linguistic terms to describe their qualitative evaluations when quantitative information is not available or the cost of obtaining quantitative information is too high [23; 24; 25]. To more accurately and objectively describe the linguistic assessment information, Pythagorean uncertain linguistic sets (PULSs) [26] were proposed recently. The desirable advantage of PULSs is that they can describe two aspects of an object: an uncertain linguistic variable and a Pythagorean fuzzy number (PFN). Since its introduction, the PULS has received widespread attention in academia and has been applied in many practical fields [27; 28]. Therefore, it is desirable to apply the PULSs to character the fuzziness and uncertainty expert knowledge in DUCGs. In addition, obtaining accurate knowledge parameters play an important role in the KRR of DUCGs. However, previous research often directly gave the values of knowledge parameters or the representation of events [20; 21; 22]. Few studies have considered obtaining the knowledge parameters of DUCG from experts' evaluations [22]. In addition, due to the different backgrounds of domain experts, experts often have diverse opinions about the knowledge parameters of a DUCG. However, existing studies fail to handle the conflicting opinions from experts on knowledge parameters. When experts have different opinions on an evaluation index, [PERSON] et al. [13] adopted the weighting method to compute probabilities in DUCG; but they did not consider conflicting and inconsistent opinions among experts. Hence, developing effective methods to handle conflict opinions of experts to obtain knowledge parameters more accurately is vital for knowledge acquisition using DUCGs. Motivated by the above analyses, this article aims to develop a new type of DUCGs, called PUL-DUCG, to enhance the ability of DUCGs in KRR.