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The intricate and heterogeneous nature introduced by the decentralized architecture of the modern electric information collection system has increased the difficulty of establishing verification index systems and quality appraisal frameworks. Furthermore, discrepancies between distinct components complicate the realization of impartial and scientifically sound validation. To address these challenges, an integrated construction quality evaluation framework grounded in multi-scenario comprehensive weighted assessments was developed by exploiting graph neural networks and siamese networks. Initially, a graph neural network was formulated utilizing terminal data from the regional branches' electricity utilization information collection system alongside its link topological framework. This network was subsequently implemented at the headquarters level to directly capture terminal information and produce mirrored network inputs from the regional branch data, facilitating the real-time acquisition of diverse system performance metrics. Ultimately, the degree of correspondence between the headquarters and regional branch datasets was computed via siamese networks to derive precision compensation weights for validating and analyzing various parameters, thereby yielding comprehensive weighted quality appraisal indicators for the next-generation electric information collection systems at the branch level. Three distinct service types—namely, power data acquisition, load forecasting, and task scheduling—served as the experimental environments. The empirical findings demonstrated that the multidimensional comprehensive weighted quality appraisal, augmented by the accuracy compensation derived from the siamese network, achieved business construction quality evaluation ratings of 97.92%, 95.95%, and 99.96% within the branch system. These metrics closely approximate the quality ratings obtained through manual assessment, confirming that this methodology can reliably validate the construction quality of modern systems implemented across branches.