High-density polyethylene pipelines have been widely used in various industries owing to their inherent advantages, such as resistance to harsh environments. However, a variety of defects are caused by welding process such as electrofusion technology. Therefore, detecting defects is necessary to guarantee welding quality and safety. The existing methods of detecting defects mainly neglect the internal spatial characteristics and depend on many rules. In this paper, detecting defect is regarded as a sequence labelling problem, and a novel spatial sequence to sequence attentive neural network is proposed to automatically identification defects via the natural spatial features of electrofusion joint. The proposed network utilizes the relative position between the neighbour resistance wire and possible defect to generate the vector representation, and expands the vectors through multivariate Gaussian probability distribution to highlight the vital sequence features related to defect. Self-attention is employed to capture the prominent distinguishing information between normal resistance wire and possible defect. The experimental results on the actual dataset collecting from electrofusion joints of high-density polyethylene illustrate that expanded vector is useful to focus on crucial features and self-attention promotes the identification ability of the proposed network, achieving the F1-score of 92.28 %.