Increasing numbers of laboratory blood parameters (BPM) have been reported to greatly affect the long-term outcomes of gastric cancer (GC) patients. However, the existing prognostic models do not comprehensively analyze these predictors. To construct a new prognostic tool, based on all the prognostic BPM, to achieve more accurate prognosis prediction for GC. We retrospectively assessed 850 consecutive patients who underwent curative resection for stage II-III GC from January 2010 to April 2013. The patients were classified into developing (n = 567) and validation (n = 283) cohorts using computer-generated random numbers. A scoring system, namely BPM score, was then constructed using least absolute shrinkage and selection operator (LASSO) Cox regression model in the developing cohort, and validated in the validation cohort. A nomogram consisting of BPM score and tumor-lymph node-metastasis (TNM) stage was further created. The discrimination and calibration of the nomogram were evaluated via Harrell's C-statistic and the Hosmer-Lemeshow test. Using the LASSO model, we established the BPM score based on five BPM: Albumin, lymphocyte-to-monocyte ratio, neutrophil-to-lymphocyte ratio, carcinoembryonic antigen, and carbohydrate antigen 19-9. The BPM scores were divided into high- and low-BPM groups based on a cut-off value of -0.93. High-BPM patients were significantly older and had more advanced, larger tumors. In the developing cohort, significant differences were found in 5-year overall survival (OS) and 5-year disease-specific survival between the high-BPM and low-BPM patients. Similar results were found in the validation group. Multivariable analysis showed that the BPM score was an independent predictor of OS. High-BPM patients had a poorer 5-year OS for each subgroup. Furthermore, a nomogram that combined the BPM score and TNM stage had significantly better prognostic value compared with TNM stage alone. The BPM score provides more accurate prognosis prediction in stage II-III GC patients and is an effective complement to the TNM staging system. ©The Author(s) 2019. Published by Baishideng Publishing Group Inc. All rights reserved.