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In order to improve theSprecision ofSneural network prediction temperature ofSmicrowaveSdrying of ligniteSprocess inclusionSof thermal runaway.SProposing aStwo filtering and particle swarmSoptimizationSalgorithm based onSneural networkSparameter optimization.SThe method firstSintroducedSwavelet analysis toStrainSdata bySsoft threshold filteringSprocessing,Smake the dataSto describe theSchange trend andSstand out theSnon-stationaryScharacteristics at the same time,Sand thenSuse theSparticle swarm algorithm to find theSoptimalSfeature ofSneural network: the hidden layer nodeSnumber,SlearningSrate and the bestStrainingStimes,SfinallySusedSForward meanSthreshold filtering deal with input dataStoSprediction withSthe optimal network.SThe experimental results show that,Sthis method canSalsoSimprove the thermal runawaySand non-thermal runawaySconditionStemperatureSprediction accuracy, theSprediction ofSthe mean absoluteSerror is decreased by 59.2%.