In the transformation of government functions to “service-oriented” construction, administrative efficiency is always the focus of the digital government project. This paper takes the collection, mining and integration of event data as the path to improve administrative efficiency under the digital government platform. The government event collection channels are sorted out and an event distribution model is formed to build a digital platform for government event distribution. Subsequently, the key technology framework for multi-source heterogeneous data fusion of sensitive data is established by cleaning inferior data and standardizing storage of integrated data. For the multi-source heterogeneous data provided by the technical framework, a heterogeneous database log parsing algorithm is proposed to meet the demand for change log data capture from multiple heterogeneous databases for the daily operation of government affairs. After completing the data preparation, a multi-source heterogeneous data mining model is constructed to carry out multi-source heterogeneous data mining based on fuzzy C-mean clustering to realize the deep mining of multi-source heterogeneous few class data sets. Compared with similar model algorithms, the data screening time of the multi-source heterogeneous data mining model is always under 20s, which assists in improving the administrative efficiency of the digital government platform with superior data processing speed.
