Phase sensitive Optical Time Domain Reflectometer (Φ-OTDR), usually addressed as Distributed Acoustic Sensor (DAS), is an emerging IoT-based sensing technology that generates large volumes of data during real-time acquisition. Artificial intelligence-driven models and methods have made accurate and real-time analysis of its disturbance events possible in recent years. However, identifying novel disturbance events still poses a key challenge. This challenge is further exacerbated under semi-supervised conditions due to the scarcity of labeled data, and becomes particularly severe in open-world scenarios, where the model must not only recognize known categories but also actively discover novel ones in the complete absence of supervision for unknown classes. This paper proposes an effective semi-supervised method—ODAS (Open-world Semi-Supervised DAS)—to adaptively recognize new categories of sensing events. The method jointly trains on class probability and embeddings to mitigate confirmation bias, leveraging graph-based contrastive learning with graph neural network-based regularization to explicitly enforce structural consistency among sample embeddings, thereby effectively alleviating representation degeneration and enhancing model robustness under sparse-label conditions. An adaptive synchronized threshold is applied to balance the learning rates of known and novel classes, accompanied by an uncertainty-guided temperature scaling to generate more reliable pseudo-labels and feature embeddings. Experimental results demonstrate that our proposed method achieves over 98% accuracy for known event classes and over 85% for novel event classes, with an overall classification accuracy exceeding 93%. This approach introduces open-world semi-supervised learning to the Φ-OTDR open application environment, overcoming the limitations of traditional closed-world scenarios. It significantly reduces data labeling costs, providing the system with enhanced recognition capabilities when encountering new types of disturbances. Particularly in unlabeled or unknown disturbance environments, this method provides superior robustness and generalization ability to sensing event classification of Φ-OTDR.