## 6 Conclusions  
Risk estimation is commonly conducted by integrating quantitative and qualitative evaluation of risk assessment group members, which may be influenced by subjective factors introduced by individuals involved in the process, leading to disagreements regarding risk estimation results and hindering a collaborative implementation of more effective risk reduction measures among all safety-related parties. The LSTM-based risk estimation method was proposed to integrate historical risk estimation data, solidify expert experience, and refine judgments based on the comprehensive analysis of historical products or applications. To facilitate the establishment of a comprehensive dataset of hazard events, the essential elements of hazardous situation descriptions was systematically categorized. A hazard event dataset of industrial robot originated from CR and CE certification was used to verify the proposed methodology. The experiment demonstrated a substantial improvement in both the accuracy and stability with the augmentation of hazard event dataset. On the new dataset, the trained network also showed near-perfect performance on similar hazard events and trustworthiness on completely different ones. In cases of risk deviations, most of them were small deviation without a noticeable bias. Risk estimation was \"black-boxed\" and \"digitized\" by the LSTM-based deep learning method. Risk group members only need to focus on identifying hazard events, without individual interpretation on the descriptions and grade definitions, or making subjective judgments and choices. Therefore, this approach effectively mitigates the influence of human factors during the risk estimation process. In addition, the historical risk estimation information can be transformed into a digital model, thereby establishing a standardized reference for subsequent risk estimation endeavors in similar domains. This facilitates the development of a unified benchmark for risk assessment within project teams, enterprises, and relevant stakeholders, fostering coordination among parties and promoting the implementation of coordinated security measures. In terms of implementation, several suggestions are proposed. The training of the network is heavily dependent on the availability and quality of historical risk information. It is advisable to create a high-quality hazard event dataset that specifically pertains to a particular product type or application field. Moreover, similarity analysis between the new event and the training dataset holds significant importance in identifying potential deviations in risk estimation. In cases where there are errors in text similarity matching or multiple matches of equal magnitude, an intervention by risk assessors becomes necessary. Limitations: The accuracy of the LSTM-based risk estimation method for new hazard events, which are completely different from existing ones, can be enhanced in future studies by incorporating novel deep learning algorithms and employing model explanation techniques. Due to the limitations in laboratory software and hardware resources, the utilization of large-scale models for optimizing risk estimation methods has yielded suboptimal results, exemplified by the Bidirectional Encoder Representations from Transformer (BERT) model at the same test environment and experiment parameters. Even so, considering diverse domains, such as ChatGPT, there is potential for advancing artificial intelligence models to achieve precise risk assessment within specific fields. Conceptualization, X.Z., A.W., K.Z. and X.H. ; Methodology, X.Z., A.W., K.Z. and X.H. ; software: X.Z. ; validation, X.Z., A.W. and K.Z. ; formal analysis, X.Z. and K.Z. ; investigation, X.Z., A.W., K.Z. and X.H. ; data curation, X.Z., A.W. and K.Z. ; writing--original draft, X.Z. and K.Z. ; funding acquisition, A.W. and X.Z. All authors have read and agreed to the published version of the manuscript.