Multi-person collaborative creation of Building Information Modeling (BIM) drawings is very important in large building projects. However, the existing methods of multi-person collaborative creation of BIM drawings based on Revit and other modeling software or cloud service have the confusion of BIM drawing version, difficulty of traceability, data security risks and other problems. To solve these problems, a blockchain-based multi-person collaborative creation system for BIM drawings was designed. By using the on-chain and off-chain collaborative storage method, the blockchain and database were used to store BIM drawings information after each creation in the BIM drawing creation process and the complete BIM drawings separately. The decentralization, traceability and anti-tampering characteristics of the blockchain were used to ensure that the version of the BIM drawings is clear, and provide a basis for the future copyright division. These characteristics were also used to enhance the data security of BIM drawings information. Experimental results show that the average block generation time of the proposed system in the multi-user concurrent case is 0.467 85 s, and the maximum processing rate of the system is 1 568 transactions per second, which prove the reliability of the system and that the system can meet the needs of actual application scenarios.

To solve the problem that semantic matching between questions and relations in a single fact-based question answering system is difficult to obtain high accuracy in small-scale labeled samples, a transfer learning model based on Recurrent Neural Network (RNN) was proposed. Firstly, by the way of reconstructing sequences, an RNN-based sequence-to-sequence unsupervised learning algorithm was used to learn the semantic distribution (word vector and RNN) of questions in a large number of unlabeled samples. Then, by assigning values to the parameters of a neural network, the semantic distribution was used as the parameters of the supervised semantic matching algorithm. Finally, by the inner product of the question features and relation features, the semantic matching model was trained and generated in labeled samples. The experimental results show that compared with the supervised learning method Embed-AVG and RNNrandom, the accuracy of semantic matching of the proposed model is averagely increased by 5.6 and 8.8 percentage points respectively in an environment with a small number of labeled samples and a large number of unlabeled samples. The proposed model can significantly improve the accuracy of semantic matching in an environment with labeled samples by pre-learning the semantic distribution of a large number of unlabeled samples.